p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
Deep Reinforcement Learning Algorithms With PyTorch
This is a PyTorch-based toolkit for training reinforcement learning agents, providing implementations of standard and hierarchical deep RL algorithms. It is designed as a library for deep reinforcement learning research and experimentation, supporting both discrete and continuous control tasks through a collection of algorithm implementations.
The project distinguishes itself by offering a hierarchical reinforcement learning framework that decomposes complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level policies. It also includes a Hindsight Experience Replay implementation that enables agents to learn from sparse or binary rewards by replaying failed trajectories as if they had achieved their goals. The toolkit supports training on custom environments that follow the standard RL interface, allowing adaptation to new tasks through configuration changes without modifying code.
The repository covers agent training on standard benchmark environments, continuous control with RL, and custom environment training. It provides the core architectural components needed for deep RL, including actor-critic dual network architectures, experience replay buffers, and target network soft updates, all built on PyTorch's automatic differentiation engine.
