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Comprehensive MADDPG 알고리즘 Tools for Every Need

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

MADDPG 알고리즘

  • MADDPG-Keras

    A Keras-based implementation of Multi-Agent Deep Deterministic Policy Gradient for cooperative and competitive multi-agent RL.
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    What is MADDPG-Keras?
    MADDPG-Keras delivers a complete framework for multi-agent reinforcement learning research by implementing the MADDPG algorithm in Keras. It supports continuous action spaces, multiple agents, and standard OpenAI Gym environments. Researchers and developers can configure neural network architectures, training hyperparameters, and reward functions, then launch experiments with built-in logging and model checkpointing to accelerate multi-agent policy learning and benchmarking.
    MADDPG-Keras Core Features
    • Keras & TensorFlow implementation of MADDPG
    • Support for continuous action spaces
    • Configurable multi-agent Gym environments
    • Logging, tensorboard integration, and checkpointing
    • Customizable neural network architectures

  • 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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