This repository provides implementations of classic deep reinforcement learning models using Tensorflow and OpenAI Gym. It is targeted at researchers and practitioners looking for clear, runnable examples of foundational RL algorithms. The benefit is a set of well-documented, reproducible experiments for learning and benchmarking.

How It Works

The project implements various deep reinforcement learning algorithms, including DQN, DDPG, A3C, and TRPO, within the OpenAI Gym framework. Models are configured via JSON files, allowing for easy experimentation with hyperparameters and environments. The architecture leverages Tensorflow for neural network computations and standard Python practices for training loops and environment interaction.

Quick Start & Requirements

• Install dependencies using pip install -e . and pip install -r requirements.txt within a Python 3.6.4 virtual environment.
• Requires OpenAI Gym installation, with optional dependencies for advanced environments like Atari.
• Training is initiated via python learn.py <config_file.json> from the playground directory.
• TensorBoard can be launched with tensorboard --logdir=logs.
• Official documentation is available via linked blog posts.

Highlighted Details

• Implements classic DRL algorithms: DQN, DDPG, A3C, TRPO.
• Configuration-driven training via JSON files.
• Generates logs, checkpoints, and figures for analysis.
• Integrates with OpenAI Gym environments.

Maintenance & Community

The repository is maintained by Lilian Weng. Further community engagement or roadmap details are not explicitly mentioned in the README.

Licensing & Compatibility

The repository's license is not specified in the README. Compatibility for commercial use or closed-source linking is therefore undetermined.

Limitations & Caveats

The project is focused on foundational algorithms and may not include the latest advancements in deep reinforcement learning. The README specifies Python 3.6.4, which may require environment management for users with newer Python installations.