Results for “persistent-state”

Train reinforcement learning agents using PPO, SAC, DQN, TD3, DDPG, and A2C algorithms with a scikit-learn-like API. Supports custom Gymnasium environments, vectorized environments, callbacks, and model persistence.

Train and run Mamba state-space models with O(n) complexity, achieving faster inference and longer context than Transformers.