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--- |
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tags: |
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- reinforcement-learning |
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- atari |
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- babyai |
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- metaworld |
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- mujoco-ant |
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- mujoco |
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datasets: jat-project/jat-dataset |
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pipeline_tag: reinforcement-learning |
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model-index: |
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- name: jat-project/jat |
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results: |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Atari 57 |
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type: atari |
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metrics: |
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- type: iqm_expert_normalized_total_reward |
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value: 0.14 [0.14, 0.15] |
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name: IQM expert normalized total reward |
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- type: iqm_human_normalized_total_reward |
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value: 0.38 [0.37, 0.39] |
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name: IQM human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: BabyAI |
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type: babyai |
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metrics: |
|
- type: iqm_expert_normalized_total_reward |
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value: 0.99 [0.99, 0.99] |
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name: IQM expert normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: MetaWorld |
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type: metaworld |
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metrics: |
|
- type: iqm_expert_normalized_total_reward |
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value: 0.65 [0.64, 0.67] |
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name: IQM expert normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: MuJoCo |
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type: mujoco |
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metrics: |
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- type: iqm_expert_normalized_total_reward |
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value: 0.85 [0.83, 0.86] |
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name: IQM expert normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Alien |
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type: atari-alien |
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metrics: |
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- type: total_reward |
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value: 1518.70 +/- 568.14 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.08 +/- 0.03 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.19 +/- 0.08 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Amidar |
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type: atari-amidar |
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metrics: |
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- type: total_reward |
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value: 89.17 +/- 78.73 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.04 +/- 0.04 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.05 +/- 0.05 |
|
name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Assault |
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type: atari-assault |
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metrics: |
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- type: total_reward |
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value: 1676.91 +/- 780.73 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.09 +/- 0.05 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 2.80 +/- 1.50 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Asterix |
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type: atari-asterix |
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metrics: |
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- type: total_reward |
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value: 844.50 +/- 546.85 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.18 +/- 0.16 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.08 +/- 0.07 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Asteroids |
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type: atari-asteroids |
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metrics: |
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- type: total_reward |
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value: 1357.90 +/- 453.01 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
|
name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Atlantis |
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type: atari-atlantis |
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metrics: |
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- type: total_reward |
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value: 51843.00 +/- 123857.07 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.13 +/- 0.40 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 2.41 +/- 7.66 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Bank Heist |
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type: atari-bankheist |
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metrics: |
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- type: total_reward |
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value: 977.80 +/- 156.49 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.74 +/- 0.12 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 1.30 +/- 0.21 |
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name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Battle Zone |
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type: atari-battlezone |
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metrics: |
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- type: total_reward |
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value: 16780.00 +/- 6926.15 |
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name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.06 +/- 0.02 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.45 +/- 0.19 |
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name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Beam Rider |
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type: atari-beamrider |
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metrics: |
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- type: total_reward |
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value: 768.36 +/- 364.06 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.02 +/- 0.02 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Berzerk |
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type: atari-berzerk |
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metrics: |
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- type: total_reward |
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value: 616.20 +/- 296.08 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.20 +/- 0.12 |
|
name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Bowling |
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type: atari-bowling |
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metrics: |
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- type: total_reward |
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value: 22.32 +/- 5.18 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.00 |
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name: Expert normalized total reward |
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- type: human_normalized_total_reward |
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value: -0.01 +/- 0.04 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Boxing |
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type: atari-boxing |
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metrics: |
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- type: total_reward |
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value: 92.31 +/- 18.24 |
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name: Total reward |
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- type: expert_normalized_total_reward |
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value: 0.94 +/- 0.19 |
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name: Expert normalized total reward |
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- type: human_normalized_total_reward |
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value: 7.68 +/- 1.52 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
|
name: Reinforcement Learning |
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dataset: |
|
name: Breakout |
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type: atari-breakout |
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metrics: |
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- type: total_reward |
|
value: 7.93 +/- 5.66 |
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name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.22 +/- 0.20 |
|
name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
|
name: Reinforcement Learning |
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dataset: |
|
name: Centipede |
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type: atari-centipede |
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metrics: |
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- type: total_reward |
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value: 5888.27 +/- 2594.62 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.40 +/- 0.27 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.38 +/- 0.26 |
|
name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
|
name: Reinforcement Learning |
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dataset: |
|
name: Chopper Command |
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type: atari-choppercommand |
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metrics: |
|
- type: total_reward |
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value: 2371.00 +/- 1195.43 |
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name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.02 +/- 0.01 |
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name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.24 +/- 0.18 |
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name: Human normalized total reward |
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- task: |
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type: reinforcement-learning |
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name: Reinforcement Learning |
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dataset: |
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name: Crazy Climber |
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type: atari-crazyclimber |
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metrics: |
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- type: total_reward |
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value: 97145.00 +/- 30388.04 |
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name: Total reward |
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- type: expert_normalized_total_reward |
|
value: 0.51 +/- 0.18 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 3.45 +/- 1.21 |
|
name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
|
name: Reinforcement Learning |
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dataset: |
|
name: Defender |
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type: atari-defender |
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metrics: |
|
- type: total_reward |
|
value: 39317.50 +/- 16246.15 |
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name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.10 +/- 0.05 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 2.30 +/- 1.03 |
|
name: Human normalized total reward |
|
- task: |
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type: reinforcement-learning |
|
name: Reinforcement Learning |
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dataset: |
|
name: Demon Attack |
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type: atari-demonattack |
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metrics: |
|
- type: total_reward |
|
value: 795.10 +/- 982.55 |
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name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.35 +/- 0.54 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Double Dunk |
|
type: atari-doubledunk |
|
metrics: |
|
- type: total_reward |
|
value: 13.40 +/- 11.07 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.81 +/- 0.28 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.91 +/- 0.32 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Enduro |
|
type: atari-enduro |
|
metrics: |
|
- type: total_reward |
|
value: 103.11 +/- 28.05 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.04 +/- 0.01 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.12 +/- 0.03 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Fishing Derby |
|
type: atari-fishingderby |
|
metrics: |
|
- type: total_reward |
|
value: -31.67 +/- 22.54 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.61 +/- 0.23 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.46 +/- 0.17 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Freeway |
|
type: atari-freeway |
|
metrics: |
|
- type: total_reward |
|
value: 27.57 +/- 1.87 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.81 +/- 0.06 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.93 +/- 0.06 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Frostbite |
|
type: atari-frostbite |
|
metrics: |
|
- type: total_reward |
|
value: 2875.60 +/- 1679.84 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.21 +/- 0.13 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.66 +/- 0.39 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Gopher |
|
type: atari-gopher |
|
metrics: |
|
- type: total_reward |
|
value: 5508.80 +/- 2802.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.06 +/- 0.03 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 2.44 +/- 1.30 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Gravitar |
|
type: atari-gravitar |
|
metrics: |
|
- type: total_reward |
|
value: 1330.50 +/- 918.23 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.30 +/- 0.24 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.36 +/- 0.29 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: H.E.R.O. |
|
type: atari-hero |
|
metrics: |
|
- type: total_reward |
|
value: 11932.00 +/- 3036.87 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.25 +/- 0.07 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.37 +/- 0.10 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Ice Hockey |
|
type: atari-icehockey |
|
metrics: |
|
- type: total_reward |
|
value: 7.61 +/- 5.28 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.52 +/- 0.15 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 1.55 +/- 0.44 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: James Bond |
|
type: atari-jamesbond |
|
metrics: |
|
- type: total_reward |
|
value: 425.00 +/- 632.51 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.02 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 1.45 +/- 2.31 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Kangaroo |
|
type: atari-kangaroo |
|
metrics: |
|
- type: total_reward |
|
value: 375.00 +/- 314.13 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.62 +/- 0.60 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.11 +/- 0.11 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Krull |
|
type: atari-krull |
|
metrics: |
|
- type: total_reward |
|
value: 10743.30 +/- 1311.26 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.93 +/- 0.13 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 8.57 +/- 1.23 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Kung-Fu Master |
|
type: atari-kungfumaster |
|
metrics: |
|
- type: total_reward |
|
value: 253.00 +/- 233.86 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: -0.00 +/- 0.01 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: -0.00 +/- 0.01 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Montezuma's Revenge |
|
type: atari-montezumarevenge |
|
metrics: |
|
- type: total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Ms. Pacman |
|
type: atari-mspacman |
|
metrics: |
|
- type: total_reward |
|
value: 1610.10 +/- 504.08 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.20 +/- 0.08 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.20 +/- 0.08 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Name This Game |
|
type: atari-namethisgame |
|
metrics: |
|
- type: total_reward |
|
value: 7726.40 +/- 2166.18 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.26 +/- 0.10 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.94 +/- 0.38 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Phoenix |
|
type: atari-phoenix |
|
metrics: |
|
- type: total_reward |
|
value: 1814.20 +/- 1275.29 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.16 +/- 0.20 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: PitFall |
|
type: atari-pitfall |
|
metrics: |
|
- type: total_reward |
|
value: -4.61 +/- 15.86 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.99 +/- 0.07 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.03 +/- 0.00 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pong |
|
type: atari-pong |
|
metrics: |
|
- type: total_reward |
|
value: 16.54 +/- 10.34 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.89 +/- 0.25 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 1.05 +/- 0.29 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Private Eye |
|
type: atari-privateeye |
|
metrics: |
|
- type: total_reward |
|
value: 44.00 +/- 49.64 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.25 +/- 0.66 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Q*Bert |
|
type: atari-qbert |
|
metrics: |
|
- type: total_reward |
|
value: 2118.50 +/- 2764.25 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.05 +/- 0.06 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.15 +/- 0.21 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: River Raid |
|
type: atari-riverraid |
|
metrics: |
|
- type: total_reward |
|
value: 3925.20 +/- 1530.94 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.19 +/- 0.11 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.16 +/- 0.10 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Road Runner |
|
type: atari-roadrunner |
|
metrics: |
|
- type: total_reward |
|
value: 6929.00 +/- 5577.35 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.09 +/- 0.07 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.88 +/- 0.71 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Robotank |
|
type: atari-robotank |
|
metrics: |
|
- type: total_reward |
|
value: 10.22 +/- 4.71 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.10 +/- 0.06 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.83 +/- 0.49 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Seaquest |
|
type: atari-seaquest |
|
metrics: |
|
- type: total_reward |
|
value: 859.80 +/- 407.80 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.31 +/- 0.16 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.02 +/- 0.01 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Skiing |
|
type: atari-skiing |
|
metrics: |
|
- type: total_reward |
|
value: -15960.04 +/- 5887.52 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.18 +/- 0.93 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.09 +/- 0.46 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Solaris |
|
type: atari-solaris |
|
metrics: |
|
- type: total_reward |
|
value: 1202.60 +/- 445.27 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: -0.29 +/- 3.79 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: -0.00 +/- 0.04 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Space Invaders |
|
type: atari-spaceinvaders |
|
metrics: |
|
- type: total_reward |
|
value: 326.85 +/- 141.89 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.00 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.12 +/- 0.09 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Star Gunner |
|
type: atari-stargunner |
|
metrics: |
|
- type: total_reward |
|
value: 5219.00 +/- 3544.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.01 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.48 +/- 0.37 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Surround |
|
type: atari-surround |
|
metrics: |
|
- type: total_reward |
|
value: 1.52 +/- 4.60 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.59 +/- 0.24 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.70 +/- 0.28 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Tennis |
|
type: atari-tennis |
|
metrics: |
|
- type: total_reward |
|
value: -12.80 +/- 3.70 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.32 +/- 0.11 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.34 +/- 0.12 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Time Pilot |
|
type: atari-timepilot |
|
metrics: |
|
- type: total_reward |
|
value: 11603.00 +/- 4323.25 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.12 +/- 0.07 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 4.84 +/- 2.60 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Tutankham |
|
type: atari-tutankham |
|
metrics: |
|
- type: total_reward |
|
value: 108.82 +/- 70.14 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.35 +/- 0.25 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.62 +/- 0.45 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Up and Down |
|
type: atari-upndown |
|
metrics: |
|
- type: total_reward |
|
value: 19074.60 +/- 9961.77 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.04 +/- 0.02 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 1.66 +/- 0.89 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Venture |
|
type: atari-venture |
|
metrics: |
|
- type: total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Video Pinball |
|
type: atari-videopinball |
|
metrics: |
|
- type: total_reward |
|
value: 12466.69 +/- 8723.07 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.03 +/- 0.02 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.71 +/- 0.49 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Wizard of Wor |
|
type: atari-wizardofwor |
|
metrics: |
|
- type: total_reward |
|
value: 2231.00 +/- 2042.92 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.03 +/- 0.04 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.40 +/- 0.49 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Yars Revenge |
|
type: atari-yarsrevenge |
|
metrics: |
|
- type: total_reward |
|
value: 11190.85 +/- 7342.58 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.03 +/- 0.03 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.16 +/- 0.14 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Zaxxon |
|
type: atari-zaxxon |
|
metrics: |
|
- type: total_reward |
|
value: 5976.00 +/- 2889.54 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.08 +/- 0.04 |
|
name: Expert normalized total reward |
|
- type: human_normalized_total_reward |
|
value: 0.65 +/- 0.32 |
|
name: Human normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Action Obj Door |
|
type: babyai-action-obj-door |
|
metrics: |
|
- type: total_reward |
|
value: 0.92 +/- 0.22 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.88 +/- 0.36 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Blocked Unlock Pickup |
|
type: babyai-blocked-unlock-pickup |
|
metrics: |
|
- type: total_reward |
|
value: 0.95 +/- 0.01 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.01 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Boss Level No Unlock |
|
type: babyai-boss-level-no-unlock |
|
metrics: |
|
- type: total_reward |
|
value: 0.49 +/- 0.43 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.49 +/- 0.49 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Boss Level |
|
type: babyai-boss-level |
|
metrics: |
|
- type: total_reward |
|
value: 0.54 +/- 0.43 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.54 +/- 0.49 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Find Obj S5 |
|
type: babyai-find-obj-s5 |
|
metrics: |
|
- type: total_reward |
|
value: 0.94 +/- 0.04 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.04 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Door |
|
type: babyai-go-to-door |
|
metrics: |
|
- type: total_reward |
|
value: 0.99 +/- 0.02 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.03 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Imp Unlock |
|
type: babyai-go-to-imp-unlock |
|
metrics: |
|
- type: total_reward |
|
value: 0.53 +/- 0.41 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.60 +/- 0.55 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Local |
|
type: babyai-go-to-local |
|
metrics: |
|
- type: total_reward |
|
value: 0.87 +/- 0.16 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.93 +/- 0.22 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Obj Door |
|
type: babyai-go-to-obj-door |
|
metrics: |
|
- type: total_reward |
|
value: 0.98 +/- 0.04 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.98 +/- 0.08 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Obj |
|
type: babyai-go-to-obj |
|
metrics: |
|
- type: total_reward |
|
value: 0.94 +/- 0.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.01 +/- 0.03 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Red Ball Grey |
|
type: babyai-go-to-red-ball-grey |
|
metrics: |
|
- type: total_reward |
|
value: 0.92 +/- 0.05 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.06 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Red Ball No Dists |
|
type: babyai-go-to-red-ball-no-dists |
|
metrics: |
|
- type: total_reward |
|
value: 0.93 +/- 0.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.03 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Red Ball |
|
type: babyai-go-to-red-ball |
|
metrics: |
|
- type: total_reward |
|
value: 0.91 +/- 0.09 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.98 +/- 0.12 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Red Blue Ball |
|
type: babyai-go-to-red-blue-ball |
|
metrics: |
|
- type: total_reward |
|
value: 0.91 +/- 0.08 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.99 +/- 0.10 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To Seq |
|
type: babyai-go-to-seq |
|
metrics: |
|
- type: total_reward |
|
value: 0.73 +/- 0.33 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.76 +/- 0.38 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Go To |
|
type: babyai-go-to |
|
metrics: |
|
- type: total_reward |
|
value: 0.78 +/- 0.28 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.82 +/- 0.35 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Key Corridor |
|
type: babyai-key-corridor |
|
metrics: |
|
- type: total_reward |
|
value: 0.87 +/- 0.13 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.96 +/- 0.14 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Mini Boss Level |
|
type: babyai-mini-boss-level |
|
metrics: |
|
- type: total_reward |
|
value: 0.53 +/- 0.41 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.56 +/- 0.50 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Move Two Across S8N9 |
|
type: babyai-move-two-across-s8n9 |
|
metrics: |
|
- type: total_reward |
|
value: 0.05 +/- 0.19 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.05 +/- 0.20 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: One Room S8 |
|
type: babyai-one-room-s8 |
|
metrics: |
|
- type: total_reward |
|
value: 0.92 +/- 0.04 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.04 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Open Door |
|
type: babyai-open-door |
|
metrics: |
|
- type: total_reward |
|
value: 0.99 +/- 0.00 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.01 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Open Doors Order N4 |
|
type: babyai-open-doors-order-n4 |
|
metrics: |
|
- type: total_reward |
|
value: 0.96 +/- 0.14 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.96 +/- 0.17 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Open Red Door |
|
type: babyai-open-red-door |
|
metrics: |
|
- type: total_reward |
|
value: 0.92 +/- 0.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.03 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Open Two Doors |
|
type: babyai-open-two-doors |
|
metrics: |
|
- type: total_reward |
|
value: 0.98 +/- 0.00 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Open |
|
type: babyai-open |
|
metrics: |
|
- type: total_reward |
|
value: 0.95 +/- 0.08 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.99 +/- 0.10 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pickup Above |
|
type: babyai-pickup-above |
|
metrics: |
|
- type: total_reward |
|
value: 0.92 +/- 0.06 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.01 +/- 0.07 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pickup Dist |
|
type: babyai-pickup-dist |
|
metrics: |
|
- type: total_reward |
|
value: 0.87 +/- 0.12 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.02 +/- 0.16 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pickup Loc |
|
type: babyai-pickup-loc |
|
metrics: |
|
- type: total_reward |
|
value: 0.85 +/- 0.19 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.92 +/- 0.23 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pickup |
|
type: babyai-pickup |
|
metrics: |
|
- type: total_reward |
|
value: 0.79 +/- 0.30 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.85 +/- 0.36 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Put Next Local |
|
type: babyai-put-next-local |
|
metrics: |
|
- type: total_reward |
|
value: 0.67 +/- 0.32 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.73 +/- 0.35 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Put Next S7N4 |
|
type: babyai-put-next |
|
metrics: |
|
- type: total_reward |
|
value: 0.85 +/- 0.25 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.89 +/- 0.26 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Synth Loc |
|
type: babyai-synth-loc |
|
metrics: |
|
- type: total_reward |
|
value: 0.77 +/- 0.34 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.78 +/- 0.43 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Synth Seq |
|
type: babyai-synth-seq |
|
metrics: |
|
- type: total_reward |
|
value: 0.57 +/- 0.43 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.58 +/- 0.49 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Synth |
|
type: babyai-synth |
|
metrics: |
|
- type: total_reward |
|
value: 0.75 +/- 0.35 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.78 +/- 0.43 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Unblock Pickup |
|
type: babyai-unblock-pickup |
|
metrics: |
|
- type: total_reward |
|
value: 0.79 +/- 0.29 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.86 +/- 0.35 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Unlock Local |
|
type: babyai-unlock-local |
|
metrics: |
|
- type: total_reward |
|
value: 0.98 +/- 0.01 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.01 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Unlock Pickup |
|
type: babyai-unlock-pickup |
|
metrics: |
|
- type: total_reward |
|
value: 0.75 +/- 0.03 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.05 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Unlock To Unlock |
|
type: babyai-unlock-to-unlock |
|
metrics: |
|
- type: total_reward |
|
value: 0.85 +/- 0.31 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.88 +/- 0.32 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Unlock |
|
type: babyai-unlock |
|
metrics: |
|
- type: total_reward |
|
value: 0.43 +/- 0.43 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.48 +/- 0.52 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Assembly |
|
type: metaworld-assembly |
|
metrics: |
|
- type: total_reward |
|
value: 243.78 +/- 10.44 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.99 +/- 0.05 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Basketball |
|
type: metaworld-basketball |
|
metrics: |
|
- type: total_reward |
|
value: 1.71 +/- 0.63 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: -0.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: BinPicking |
|
type: metaworld-bin-picking |
|
metrics: |
|
- type: total_reward |
|
value: 314.42 +/- 196.40 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.74 +/- 0.46 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Box Close |
|
type: metaworld-box-close |
|
metrics: |
|
- type: total_reward |
|
value: 482.86 +/- 146.37 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.93 +/- 0.34 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Button Press Topdown Wall |
|
type: metaworld-button-press-topdown-wall |
|
metrics: |
|
- type: total_reward |
|
value: 268.30 +/- 82.78 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.51 +/- 0.18 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Button Press Topdown |
|
type: metaworld-button-press-topdown |
|
metrics: |
|
- type: total_reward |
|
value: 269.14 +/- 82.81 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.52 +/- 0.18 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Button Press Wall |
|
type: metaworld-button-press-wall |
|
metrics: |
|
- type: total_reward |
|
value: 608.87 +/- 169.50 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.90 +/- 0.25 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Button Press |
|
type: metaworld-button-press |
|
metrics: |
|
- type: total_reward |
|
value: 624.03 +/- 73.53 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.97 +/- 0.12 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Coffee Button |
|
type: metaworld-coffee-button |
|
metrics: |
|
- type: total_reward |
|
value: 334.92 +/- 301.67 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.43 +/- 0.43 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Coffee Pull |
|
type: metaworld-coffee-pull |
|
metrics: |
|
- type: total_reward |
|
value: 38.00 +/- 63.97 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.13 +/- 0.25 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Coffee Push |
|
type: metaworld-coffee-push |
|
metrics: |
|
- type: total_reward |
|
value: 151.38 +/- 207.69 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.30 +/- 0.42 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Dial Turn |
|
type: metaworld-dial-turn |
|
metrics: |
|
- type: total_reward |
|
value: 752.25 +/- 138.50 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.95 +/- 0.18 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Disassemble |
|
type: metaworld-disassemble |
|
metrics: |
|
- type: total_reward |
|
value: 40.87 +/- 9.35 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.22 +/- 3.71 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Door Close |
|
type: metaworld-door-close |
|
metrics: |
|
- type: total_reward |
|
value: 530.48 +/- 29.02 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.06 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Door Lock |
|
type: metaworld-door-lock |
|
metrics: |
|
- type: total_reward |
|
value: 678.98 +/- 194.57 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.81 +/- 0.28 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Door Open |
|
type: metaworld-door-open |
|
metrics: |
|
- type: total_reward |
|
value: 574.71 +/- 50.82 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.99 +/- 0.10 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Door Unlock |
|
type: metaworld-door-unlock |
|
metrics: |
|
- type: total_reward |
|
value: 761.82 +/- 114.70 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.94 +/- 0.16 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Drawer Close |
|
type: metaworld-drawer-close |
|
metrics: |
|
- type: total_reward |
|
value: 519.05 +/- 154.38 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.54 +/- 0.21 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Drawer Open |
|
type: metaworld-drawer-open |
|
metrics: |
|
- type: total_reward |
|
value: 486.02 +/- 34.17 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.98 +/- 0.09 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Faucet Close |
|
type: metaworld-faucet-close |
|
metrics: |
|
- type: total_reward |
|
value: 366.78 +/- 86.77 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.23 +/- 0.17 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Faucet Open |
|
type: metaworld-faucet-open |
|
metrics: |
|
- type: total_reward |
|
value: 685.01 +/- 65.52 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.96 +/- 0.14 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Hammer |
|
type: metaworld-hammer |
|
metrics: |
|
- type: total_reward |
|
value: 678.36 +/- 79.36 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.98 +/- 0.13 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Hand Insert |
|
type: metaworld-hand-insert |
|
metrics: |
|
- type: total_reward |
|
value: 695.27 +/- 134.25 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.94 +/- 0.18 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Handle Press Side |
|
type: metaworld-handle-press-side |
|
metrics: |
|
- type: total_reward |
|
value: 65.07 +/- 69.65 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.09 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Handle Press |
|
type: metaworld-handle-press |
|
metrics: |
|
- type: total_reward |
|
value: 695.97 +/- 311.48 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.79 +/- 0.40 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Handle Pull Side |
|
type: metaworld-handle-pull-side |
|
metrics: |
|
- type: total_reward |
|
value: 145.34 +/- 179.01 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.37 +/- 0.47 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Handle Pull |
|
type: metaworld-handle-pull |
|
metrics: |
|
- type: total_reward |
|
value: 514.56 +/- 205.75 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.77 +/- 0.31 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Lever Pull |
|
type: metaworld-lever-pull |
|
metrics: |
|
- type: total_reward |
|
value: 250.51 +/- 220.33 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.34 +/- 0.40 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Peg Insert Side |
|
type: metaworld-peg-insert-side |
|
metrics: |
|
- type: total_reward |
|
value: 305.94 +/- 166.53 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.97 +/- 0.53 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Peg Unplug Side |
|
type: metaworld-peg-unplug-side |
|
metrics: |
|
- type: total_reward |
|
value: 120.73 +/- 169.26 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.26 +/- 0.37 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pick Out Of Hole |
|
type: metaworld-pick-out-of-hole |
|
metrics: |
|
- type: total_reward |
|
value: 2.08 +/- 0.05 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.00 +/- 0.00 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pick Place Wall |
|
type: metaworld-pick-place-wall |
|
metrics: |
|
- type: total_reward |
|
value: 62.30 +/- 131.13 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.14 +/- 0.29 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pick Place |
|
type: metaworld-pick-place |
|
metrics: |
|
- type: total_reward |
|
value: 311.95 +/- 180.95 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.74 +/- 0.43 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Plate Slide Back Side |
|
type: metaworld-plate-slide-back-side |
|
metrics: |
|
- type: total_reward |
|
value: 689.54 +/- 157.90 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.94 +/- 0.23 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Plate Slide Back |
|
type: metaworld-plate-slide-back |
|
metrics: |
|
- type: total_reward |
|
value: 197.00 +/- 1.58 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.24 +/- 0.00 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Plate Slide Side |
|
type: metaworld-plate-slide-side |
|
metrics: |
|
- type: total_reward |
|
value: 122.56 +/- 24.56 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.16 +/- 0.04 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Plate Slide |
|
type: metaworld-plate-slide |
|
metrics: |
|
- type: total_reward |
|
value: 456.66 +/- 198.51 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.84 +/- 0.44 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Push Back |
|
type: metaworld-push-back |
|
metrics: |
|
- type: total_reward |
|
value: 71.38 +/- 100.60 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.84 +/- 1.20 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Push Wall |
|
type: metaworld-push-wall |
|
metrics: |
|
- type: total_reward |
|
value: 216.66 +/- 256.33 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.28 +/- 0.35 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Push |
|
type: metaworld-push |
|
metrics: |
|
- type: total_reward |
|
value: 583.25 +/- 296.10 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.78 +/- 0.40 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Reach Wall |
|
type: metaworld-reach-wall |
|
metrics: |
|
- type: total_reward |
|
value: 681.90 +/- 186.63 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.89 +/- 0.31 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Reach |
|
type: metaworld-reach |
|
metrics: |
|
- type: total_reward |
|
value: 347.45 +/- 190.66 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.37 +/- 0.36 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Shelf Place |
|
type: metaworld-shelf-place |
|
metrics: |
|
- type: total_reward |
|
value: 60.57 +/- 97.16 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.25 +/- 0.40 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Soccer |
|
type: metaworld-soccer |
|
metrics: |
|
- type: total_reward |
|
value: 309.21 +/- 172.64 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.82 +/- 0.47 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Stick Pull |
|
type: metaworld-stick-pull |
|
metrics: |
|
- type: total_reward |
|
value: 364.98 +/- 234.82 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.70 +/- 0.45 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Stick Push |
|
type: metaworld-stick-push |
|
metrics: |
|
- type: total_reward |
|
value: 91.05 +/- 204.71 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.14 +/- 0.33 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Sweep Into |
|
type: metaworld-sweep-into |
|
metrics: |
|
- type: total_reward |
|
value: 714.98 +/- 209.19 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.89 +/- 0.27 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Sweep |
|
type: metaworld-sweep |
|
metrics: |
|
- type: total_reward |
|
value: 15.82 +/- 16.34 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.01 +/- 0.03 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Window Close |
|
type: metaworld-window-close |
|
metrics: |
|
- type: total_reward |
|
value: 347.90 +/- 222.50 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.54 +/- 0.42 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Window Open |
|
type: metaworld-window-open |
|
metrics: |
|
- type: total_reward |
|
value: 574.72 +/- 75.65 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.97 +/- 0.14 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Ant |
|
type: mujoco-ant |
|
metrics: |
|
- type: total_reward |
|
value: 4993.13 +/- 1656.89 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.86 +/- 0.28 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Inverted Double Pendulum |
|
type: mujoco-doublependulum |
|
metrics: |
|
- type: total_reward |
|
value: 8744.92 +/- 1471.45 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.94 +/- 0.16 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Half Cheetah |
|
type: mujoco-halfcheetah |
|
metrics: |
|
- type: total_reward |
|
value: 6601.12 +/- 488.36 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.89 +/- 0.06 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Hopper |
|
type: mujoco-hopper |
|
metrics: |
|
- type: total_reward |
|
value: 1435.45 +/- 361.77 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.77 +/- 0.20 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Humanoid |
|
type: mujoco-humanoid |
|
metrics: |
|
- type: total_reward |
|
value: 695.92 +/- 115.07 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.09 +/- 0.02 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Inverted Pendulum |
|
type: mujoco-pendulum |
|
metrics: |
|
- type: total_reward |
|
value: 117.64 +/- 21.73 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.24 +/- 0.05 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Pusher |
|
type: mujoco-pusher |
|
metrics: |
|
- type: total_reward |
|
value: -24.93 +/- 6.47 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.05 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Reacher |
|
type: mujoco-reacher |
|
metrics: |
|
- type: total_reward |
|
value: -5.77 +/- 2.27 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.00 +/- 0.06 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Humanoid Standup |
|
type: mujoco-standup |
|
metrics: |
|
- type: total_reward |
|
value: 113587.22 +/- 21821.69 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.33 +/- 0.09 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Swimmer |
|
type: mujoco-swimmer |
|
metrics: |
|
- type: total_reward |
|
value: 94.08 +/- 3.94 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 1.02 +/- 0.04 |
|
name: Expert normalized total reward |
|
- task: |
|
type: reinforcement-learning |
|
name: Reinforcement Learning |
|
dataset: |
|
name: Walker 2d |
|
type: mujoco-walker |
|
metrics: |
|
- type: total_reward |
|
value: 4381.69 +/- 848.39 |
|
name: Total reward |
|
- type: expert_normalized_total_reward |
|
value: 0.95 +/- 0.18 |
|
name: Expert normalized total reward |
|
--- |
|
|
|
# Model Card for Jat |
|
|
|
This is a multi-modal and multi-task model. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
- **Developed by:** The JAT Team |
|
- **License:** Apache 2.0 |
|
|
|
### Model Sources |
|
|
|
- **Repository:** <https://github.com/huggingface/jat> |
|
- **Paper:** <https://huggingface.co/papers/2402.09844> |
|
- **Demo:** Coming soon |
|
|
|
## Training |
|
|
|
<details> |
|
<summary>The model was trained on the following tasks:</summary> |
|
|
|
- Alien |
|
- Amidar |
|
- Assault |
|
- Asterix |
|
- Asteroids |
|
- Atlantis |
|
- Bank Heist |
|
- Battle Zone |
|
- Beam Rider |
|
- Berzerk |
|
- Bowling |
|
- Boxing |
|
- Breakout |
|
- Centipede |
|
- Chopper Command |
|
- Crazy Climber |
|
- Defender |
|
- Demon Attack |
|
- Double Dunk |
|
- Enduro |
|
- Fishing Derby |
|
- Freeway |
|
- Frostbite |
|
- Gopher |
|
- Gravitar |
|
- H.E.R.O. |
|
- Ice Hockey |
|
- James Bond |
|
- Kangaroo |
|
- Krull |
|
- Kung-Fu Master |
|
- Montezuma's Revenge |
|
- Ms. Pacman |
|
- Name This Game |
|
- Phoenix |
|
- PitFall |
|
- Pong |
|
- Private Eye |
|
- Q*Bert |
|
- River Raid |
|
- Road Runner |
|
- Robotank |
|
- Seaquest |
|
- Skiing |
|
- Solaris |
|
- Space Invaders |
|
- Star Gunner |
|
- Surround |
|
- Tennis |
|
- Time Pilot |
|
- Tutankham |
|
- Up and Down |
|
- Venture |
|
- Video Pinball |
|
- Wizard of Wor |
|
- Yars Revenge |
|
- Zaxxon |
|
- Action Obj Door |
|
- Blocked Unlock Pickup |
|
- Boss Level No Unlock |
|
- Boss Level |
|
- Find Obj S5 |
|
- Go To Door |
|
- Go To Imp Unlock |
|
- Go To Local |
|
- Go To Obj Door |
|
- Go To Obj |
|
- Go To Red Ball Grey |
|
- Go To Red Ball No Dists |
|
- Go To Red Ball |
|
- Go To Red Blue Ball |
|
- Go To Seq |
|
- Go To |
|
- Key Corridor |
|
- Mini Boss Level |
|
- Move Two Across S8N9 |
|
- One Room S8 |
|
- Open Door |
|
- Open Doors Order N4 |
|
- Open Red Door |
|
- Open Two Doors |
|
- Open |
|
- Pickup Above |
|
- Pickup Dist |
|
- Pickup Loc |
|
- Pickup |
|
- Put Next Local |
|
- Put Next S7N4 |
|
- Synth Loc |
|
- Synth Seq |
|
- Synth |
|
- Unblock Pickup |
|
- Unlock Local |
|
- Unlock Pickup |
|
- Unlock To Unlock |
|
- Unlock |
|
- Assembly |
|
- Basketball |
|
- BinPicking |
|
- Box Close |
|
- Button Press Topdown Wall |
|
- Button Press Topdown |
|
- Button Press Wall |
|
- Button Press |
|
- Coffee Button |
|
- Coffee Pull |
|
- Coffee Push |
|
- Dial Turn |
|
- Disassemble |
|
- Door Close |
|
- Door Lock |
|
- Door Open |
|
- Door Unlock |
|
- Drawer Close |
|
- Drawer Open |
|
- Faucet Close |
|
- Faucet Open |
|
- Hammer |
|
- Hand Insert |
|
- Handle Press Side |
|
- Handle Press |
|
- Handle Pull Side |
|
- Handle Pull |
|
- Lever Pull |
|
- Peg Insert Side |
|
- Peg Unplug Side |
|
- Pick Out Of Hole |
|
- Pick Place Wall |
|
- Pick Place |
|
- Plate Slide Back Side |
|
- Plate Slide Back |
|
- Plate Slide Side |
|
- Plate Slide |
|
- Push Back |
|
- Push Wall |
|
- Push |
|
- Reach Wall |
|
- Reach |
|
- Shelf Place |
|
- Soccer |
|
- Stick Pull |
|
- Stick Push |
|
- Sweep Into |
|
- Sweep |
|
- Window Close |
|
- Window Open |
|
- Ant |
|
- Inverted Double Pendulum |
|
- Half Cheetah |
|
- Hopper |
|
- Humanoid |
|
- Inverted Pendulum |
|
- Pusher |
|
- Reacher |
|
- Humanoid Standup |
|
- Swimmer |
|
- Walker 2d |
|
|
|
</details> |
|
|
|
## How to Get Started with the Model |
|
|
|
Use the code below to get started with the model. |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM |
|
|
|
model = AutoModelForCausalLM.from_pretrained("jat-project/jat") |
|
``` |
|
|
|
## Citation |
|
|
|
|
|
```bibtex |
|
@article{gallouedec2024jack, |
|
title = {{Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent}}, |
|
author = {Gallouédec, Quentin and Beeching, Edward and Romac, Clément and Dellandréa, Emmanuel}, |
|
journal = {arXiv preprint arXiv:2402.09844}, |
|
year = {2024}, |
|
url = {https://arxiv.org/abs/2402.09844} |
|
} |
|
``` |
|
|
