Answers.Body: <p>According to <a href="https://arxiv.org/abs/1903.08254" rel="nofollow noreferrer">this paper</a> (PEARL):</p>
  <blockquote>
  <p>These locomotion task families require adaptation across reward
  functions (walking direction for Half-CheetahFwd-Back, Ant-Fwd-Back,
  Humanoid-Direc-2D, target velocity for Half-Cheetah-Vel, and goal
  location for Ant-Goal2D) or across dynamics (random system parameters
  for Walker-2D-Params).</p>
  </blockquote>
  <p>It looks like different versions of the same environment with differing reward functions are used. For eg., Forward direction might be rewarded positively in one version, Negative in another, Both directions rewarded positively in yet another.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-30T13:36:01.240Z
Answers.LastActivityDate: 2020-07-30T13:36:01.240Z
Answers.UserDisplayName: Joel Joseph
Body: <p>Afaik, investigating meta reinforcement learning algorithms requires a collection of two or more environments which have similar structure but are still different enough.
  When I read <a href="https://arxiv.org/abs/1703.03400" rel="nofollow noreferrer">this paper</a> it was unclear to me what the meta-training and meta-testing environments were.</p>
  <p>For eg., a graph is given for Ant-Fwd-Bkwd showing its performance with number of gradient steps. I'm guessing these are the meta-testing performances. So, which environment was it 'meta-trained' on?</p>
  <p>Was it meta-trained on the same Ant-Fwd-Bkwd environment?</p>
  
Tags: <reinforcement-learning><meta-learning>
Title: How are mujoco environments used for meta-rl?
UserDisplayName: Joel Joseph
ThreadText: # How are mujoco environments used for meta-rl?
  
  (Asked by: username_0 on 2020-07-30)
  
  Afaik, investigating meta reinforcement learning algorithms requires a collection of two or more environments which have similar structure but are still different enough.
  When I read [this paper](https://arxiv.org/abs/1703.03400) it was unclear to me what the meta-training and meta-testing environments were.
  
  For eg., a graph is given for Ant-Fwd-Bkwd showing its performance with number of gradient steps. I'm guessing these are the meta-testing performances. So, which environment was it 'meta-trained' on?
  
  Was it meta-trained on the same Ant-Fwd-Bkwd environment?
  
  **Tags:** reinforcement-learning, meta-learning
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2020-07-30. Score: 0 | Accepted answer
  
  According to [this paper](https://arxiv.org/abs/1903.08254) (PEARL):
  
  > These locomotion task families require adaptation across reward
  > functions (walking direction for Half-CheetahFwd-Back, Ant-Fwd-Back,
  > Humanoid-Direc-2D, target velocity for Half-Cheetah-Vel, and goal
  > location for Ant-Goal2D) or across dynamics (random system parameters
  > for Walker-2D-Params).
  
  It looks like different versions of the same environment with differing reward functions are used. For eg., Forward direction might be rewarded positively in one version, Negative in another, Both directions rewarded positively in yet another.
  
  
  ---
  
  