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Comprehensive prototipagem RL Tools for Every Need

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

prototipagem RL

  • MultiAgentModel

    A Python framework enabling the design, simulation, and reinforcement learning of cooperative multi-agent systems.
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    What is MultiAgentModel?
    MultiAgentModel provides a unified API to define custom environments and agent classes for multi-agent scenarios. Developers can specify observation and action spaces, reward structures, and communication channels. Built-in support for popular RL algorithms like PPO, DQN, and A2C allows training with minimal configuration. Real-time visualization tools help monitor agent interactions and performance metrics. The modular architecture ensures easy integration of new algorithms and custom modules. It also includes a flexible configuration system for hyperparameter tuning, logging utilities for experiment tracking, and compatibility with OpenAI Gym environments for seamless portability. Users can collaborate on shared environments and replay logged sessions for analysis.
    MultiAgentModel Core Features
    • Modular environment and agent definitions
    • Support for PPO, DQN, A2C algorithms
    • Customizable reward functions and communication
    • Real-time visualization of agent interactions
    • Hyperparameter configuration and logging utilities
    • OpenAI Gym compatibility

  • 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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