This repository provides a PyTorch implementation of several deep reinforcement learning algorithms, targeting researchers and practitioners in AI and machine learning. It offers a flexible framework for training agents on various environments, with built-in visualization capabilities via Visdom, enabling efficient experimentation and analysis of RL agent performance.

How It Works

The project utilizes a factory pattern for modularity, abstracting environment, model, memory, and agent components. This design allows users to easily configure and switch between different RL algorithms (DQN, Double DQN, Dueling DQN, A3C, ACER) and OpenAI Gym environments by modifying a single configuration file (./utils/options.py). Training progress and agent performance are logged and visualized in real-time using Visdom.

Quick Start & Requirements

• Install: pip install -r requirements.txt (assuming requirements.txt exists, otherwise manual installation of dependencies).
• Prerequisites: PyTorch >= v0.2.0, Visdom, OpenAI Gym >= v0.9.0. mujoco-py is optional for continuous action spaces.
• Run: Configure training parameters in ./utils/options.py and execute python main.py.
• Visualization: Start Visdom server with python -m visdom.server.
• Docs: https://github.com/jingweiz/pytorch-rl

Highlighted Details

• Implements DQN, Double DQN, Dueling DQN, A3C (discrete/continuous), and ACER (discrete).
• Real-time training visualization with Visdom.
• Factory pattern for easy agent/environment configuration.
• Includes bonus scripts for evaluating and comparing training logs.

Maintenance & Community

The repository was last updated in 2017. It cites several influential RL repositories and authors, indicating it was developed with awareness of the state-of-the-art at that time. No active community channels or recent development activity are apparent.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. This lack of clear licensing may pose restrictions on commercial use or integration into closed-source projects.

Limitations & Caveats

The project appears to be from 2017, and dependencies like PyTorch v0.2.0 are significantly outdated, likely requiring substantial updates for compatibility with current PyTorch versions and Python environments. The lack of explicit licensing is a significant caveat for adoption.