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Showcase TRPL in README

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Dominik Moritz Roth 2024-05-31 18:25:42 +02:00
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@ -23,11 +23,11 @@ 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. 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 ```python
from fancy_rl import PPO from fancy_rl import PPO, TRPL
ppo = PPO("CartPole-v1") model = TRPL("CartPole-v1")
ppo.train() model.train()
``` ```
For environments, you can pass any [gymnasium](https://gymnasium.farama.org/) or [Fancy Gym](https://alrhub.github.io/fancy_gym/) environment ID as a string, a function returning a gymnasium environment, or an already instantiated gymnasium environment. Future plans include supporting other torchrl environments. For environments, you can pass any [gymnasium](https://gymnasium.farama.org/) or [Fancy Gym](https://alrhub.github.io/fancy_gym/) environment ID as a string, a function returning a gymnasium environment, or an already instantiated gymnasium environment. Future plans include supporting other torchrl environments.