Major improvements

New features:

• New algorithm: Deep RL from Human Preferences (thanks to @ejnnr @norabelrose et al)
• Notebooks with examples (thanks to @ernestum)
• Serialized trajectories using NumPy arrays rather than pickles, ensuring stability across versions and saving space on disk (thanks to @norabelrose)
• Weights and Biases logging support (thanks to @yawen-d)

Improvements:

• Port MCE IRL from JAX to Torch, eliminating the JAX dependency. (thanks to @qxcv)
• Refactor RewardNet code to be independent from AIRL, and shared across algorithms. (thanks to @ejnnr)
• Add Windows support including continuous integration. (thanks to @taufeeque9)