dodox/fancy_rl
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Updated README

Browse Source
This commit is contained in:
Dominik Moritz Roth 2024-05-31 13:05:11 +02:00
parent 0808655136
commit 360d2569f0
1 changed files with 13 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

35
README.md
View File

@ -6,7 +6,7 @@
<br><br>
</h1>
Fancy RL is a minimalistic and efficient implementation of Proximal Policy Optimization (PPO) and Trust Region Policy Layers (TRPL) using primitives from [torchrl](https://pypi.org/project/torchrl/). Future plans include implementing Soft Actor-Critic (SAC). This library focuses on providing clean, understandable code and reusable modules while leveraging the powerful functionalities of torchrl. We provide optional integration with wandb.
Fancy RL provides a minimalistic and efficient implementation of Proximal Policy Optimization (PPO) and Trust Region Policy Layers (TRPL) using primitives from [torchrl](https://pypi.org/project/torchrl/). This library focuses on providing clean, understandable code and reusable modules while leveraging the powerful functionalities of torchrl.
## Installation
@ -22,28 +22,19 @@ Fancy RL provides two main components:
1. **Ready-to-use Classes for PPO / TRPL**: These classes allow you to quickly get started with reinforcement learning algorithms, enjoying the performance and hackability that comes with using TorchRL.
```python
from fancy_rl.ppo import PPO
from fancy_rl.policy import Policy
import gymnasium as gym
def env_fn():
return gym.make("CartPole-v1")
# Create policy
env = env_fn()
policy = Policy(env.observation_space, env.action_space)
# Create PPO instance with default config
ppo = PPO(policy=policy, env_fn=env_fn)
# Train the agent
ppo.train()
```
```python
from ppo import PPO
import gymnasium as gym
For environments, you can pass any torchrl environments, gymnasium environments (which we handle with a compatibility layer), or a string which we will interpret as a gymnasium ID.
env_spec = "CartPole-v1"
ppo = PPO(env_spec)
ppo.train()
```
2. **Additional Modules for TRPL**: Designed to integrate with torchrl's primitives-first approach, these modules are ideal for building custom algorithms with precise trust region projections. For detailed documentation, refer to the [docs](#).
For environments, you can pass any gymnasium environment ID as a string, a function returning a gymnasium environment, or an already instantiated gymnasium environment. Future plans include supporting other torchrl environments.
Check 'example/example.py' for a more complete usage example.
2. **Additional Modules for TRPL**: Designed to integrate with torchrl's primitives-first approach, these modules are ideal for building custom algorithms with precise trust region projections.
### Background on Trust Region Policy Layers (TRPL)
@ -65,4 +56,4 @@ Contributions are welcome! Feel free to open issues or submit pull requests to e
## License
This project is licensed under the MIT License.
This project is licensed under the MIT License.