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Comprehensive DQN implementation Tools for Every Need

Get access to DQN implementation solutions that address multiple requirements. One-stop resources for streamlined workflows.

DQN implementation

  • RL-Agents

    Open-source PyTorch library providing modular implementations of reinforcement learning agents like DQN, PPO, SAC, and more.
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    What is RL-Agents?
    RL-Agents is a research-grade reinforcement learning framework built on PyTorch that bundles popular RL algorithms across value-based, policy-based, and actor-critic methods. The library features a modular agent API, GPU acceleration, seamless integration with OpenAI Gym, and built-in logging and visualization tools. Users can configure hyperparameters, customize training loops, and benchmark performance with a few lines of code, making RL-Agents ideal for academic research, prototyping, and industrial experimentation.

  • YGO-Agent

    An open-source RL agent for Yu-Gi-Oh duels, providing environment simulation, policy training, and strategy optimization.
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    What is YGO-Agent?
    The YGO-Agent framework allows researchers and enthusiasts to develop AI bots that play the Yu-Gi-Oh card game using reinforcement learning. It wraps the YGOPRO game simulator into an OpenAI Gym-compatible environment, defining state representations such as hand, field, and life points, and action representations including summoning, spell/trap activation, and attacking. Rewards are based on win/loss outcomes, damage dealt, and game progress. The agent architecture uses PyTorch to implement DQN, with options for custom network architectures, experience replay, and epsilon-greedy exploration. Logging modules record training curves, win rates, and detailed move logs for analysis. The framework is modular, enabling users to replace or extend components such as the reward function or action space.

  • Multi-Agent Reinforcement Learning

    An open-source framework for training and evaluating cooperative and competitive multi-agent reinforcement learning algorithms across diverse environments.
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    What is Multi-Agent Reinforcement Learning?
    Multi-Agent Reinforcement Learning by alaamoheb is a comprehensive open-source library designed to facilitate the development, training, and evaluation of multiple agents acting in shared environments. It includes modular implementations of value-based and policy-based algorithms such as DQN, PPO, MADDPG, and more. The repository supports integration with OpenAI Gym, Unity ML-Agents, and the StarCraft Multi-Agent Challenge, allowing users to experiment in both research and real-world inspired scenarios. With configurable YAML-based experiment setups, logging utilities, and visualization tools, practitioners can monitor learning curves, tune hyperparameters, and compare different algorithms. This framework accelerates experimentation in cooperative, competitive, and mixed multi-agent tasks, streamlining reproducible research and benchmarking.
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