This repository provides PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms, targeting researchers and practitioners in single-agent and multi-agent RL. It offers a modular design for code sharing across algorithms, simplifying experimentation and development.

How It Works

The project structures each DRL algorithm as a learning agent with a unified interface. Key components include interact for experience collection (supporting single or multiple steps), train for model updates, exploration_action for action selection with noise, action for deterministic execution, and value for state-action value estimation. This modularity facilitates code reuse and easier integration of new algorithms.

Quick Start & Requirements

• Primary install/run command: python run_a2c.py (example for A2C)
• Prerequisites: Python 3.6, PyTorch, OpenAI Gym.
• Links: Ilya Kostrikov's pytorch-a2c-ppo-acktr (for KFAC optimizer inspiration).

Highlighted Details

• Implements A2C, ACKTR, DQN, DDPG, and PPO.
• Modular agent design for code sharing.
• Supports single-step and n-step experience collection.
• Acknowledges inspiration from ikostrikov/pytorch-a2c-ppo-acktr.

Maintenance & Community

• No explicit mention of active maintenance, contributors, or community channels (Discord/Slack).
• Roadmap is implied by the "TODO" list including TRPO and LOLA.

Licensing & Compatibility

• License: MIT.
• Compatible with commercial use and closed-source linking due to the permissive MIT license.

Limitations & Caveats

The README explicitly states that reproducing results is difficult due to variations in random seeds and hyperparameters. The project is also missing several planned algorithms (TRPO, LOLA) as indicated in the TODO list.