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.
Features
- Actor-Critic Architectures - Implements actor-critic dual network architectures for continuous action selection and value estimation.
- Continuous Control Training - Applies deep RL algorithms to continuous action space problems such as robotic control and locomotion.
- Deep Reinforcement Learning Implementations - Implements and compares deep RL algorithms like DQN, DDPG, PPO, and SAC on standard benchmarks.
- Experience Replay Buffers - Provides experience replay buffers that store and sample past transitions to stabilize learning.
- Meta-Controller Stacks - Implements a hierarchical meta-controller stack that decomposes tasks into sub-goals for long-horizon problems.
- Custom Environment Adapters - Provides adapters to train agents on any custom environment following the standard RL interface.
- Hindsight Replay Learners - Implements Hindsight Experience Replay to learn from sparse rewards by replaying failed trajectories as successes.
- Hierarchical Goal Decomposers - Implements a hierarchical meta-controller that decomposes complex tasks into sub-goals for RL agents.
- Environment Wrappers - Standardizes environment interactions through a Gym-compatible wrapper interface for observation and reward handling.
- Hierarchical Reinforcement Learning Frameworks - Provides a hierarchical RL framework that decomposes complex tasks into sub-goals using meta-controllers.
- Experience Replay Implementations - Implements Hindsight Experience Replay to enable learning from sparse rewards by replaying failed trajectories.
- Goal Replay Techniques - Implements Hindsight Experience Replay to learn from sparse rewards by replaying failed trajectories as successes.
- Reinforcement Learning Training - Trains deep RL agents on discrete or continuous control tasks using standard benchmark environments.
- Hierarchical Training Frameworks - Ships a hierarchical reinforcement learning framework for training agents on long-horizon tasks.
- PyTorch Reinforcement Learning Libraries - Provides a PyTorch library with implementations of standard deep RL algorithms and environment interfaces.
- Target Network Synchronization - Implements target network soft updates to stabilize Q-value estimation during reinforcement learning.
- Task Decompositions - Breaks complex long-horizon tasks into manageable sub-goals using meta-controllers and lower-level controllers.
- Experience Replay Methods - Implements experience replay methods that store and reuse past transitions for stable RL training.
- Custom Environment Builders - Supports training agents on user-defined environments that follow the standard RL interface.
- Configuration-Driven Training Runners - Enables training on custom environments by changing configuration fields without code modifications.
- Custom Environment Training Toolkits - Provides a toolkit for training RL agents on custom environments through configuration without code changes.
- Reinforcement Learning - Comprehensive library of deep reinforcement learning implementations using PyTorch.
