File size: 1,656 Bytes
f317dd2 16078a4 f317dd2 7f427f3 f317dd2 2e140d5 f317dd2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
---
tags:
- Taxi-v3
- reinforcement-learning
- rl-framework
model-index:
- name: PPO-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.72 +/- 2.66
name: mean_reward
verified: false
---
# PPO agent playing on *Taxi-v3*
This is a trained model of an agent playing on the environment *Taxi-v3*.
The agent was trained with a PPO algorithm and evaluated for 100 episodes.
See further agent and evaluation metadata in the according README section.
## Import
The Python module used for training and uploading/downloading is [rl-framework](https://github.com/alexander-zap/rl-framework).
It is an easy-to-read, plug-and-use Reinforcement Learning framework and provides standardized interfaces
and implementations to various Reinforcement Learning methods and environments.
Also it provides connectors for the upload and download to popular model version control systems,
including the HuggingFace Hub.
## Usage
```python
from rl_framework import StableBaselinesAgent, StableBaselinesAlgorithm
# Create new agent instance
agent = StableBaselinesAgent(
algorithm=StableBaselinesAlgorithm.PPO
algorithm_parameters={
...
},
)
# Download existing agent from HF Hub
repository_id = "zap-thamm/PPO-Taxi-v3"
file_name = "algorithm.zip"
agent.download(repository_id=repository_id, filename=file_name)
```
Further examples can be found in the [exploration section of the rl-framework repository](https://github.com/alexander-zap/rl-framework/tree/main/exploration).
|