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Comprehensive implémentation de DQN Tools for Every Need

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

implémentation de DQN

  • 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.

  • 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.
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