Open
Description
Proposal
To encourage the use of Gymnasium and build up the RL community, I would propose that a large range of tutorials are created.
This is a list of tutorials that could be made
- Implementation of a custom environment
- Frozenlake training results with different map sizes
- Gymnasium vectorisation (
gym.make_vec
) - Training agents for the blackjack environment, it has Tuple observation space
- DQN for atari implementation (doesn't need to be fast)
- Training a deep RL agent with pytorch from scratch
- Training a deep RL agent with jax from scratch
- How to use the action sample masking, with example from Taxi
- Car racing, comparing agents with continuous and discrete action spaces
- Exploring the impact of bipedal walker
hardcore
parameter on agent performance - Experimenting with classic control reset options random state bounds
- Add environment or example using the Graph space