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Comprehensive fonctions de récompense personnalisables Tools for Every Need

Get access to fonctions de récompense personnalisables solutions that address multiple requirements. One-stop resources for streamlined workflows.

fonctions de récompense personnalisables

  • MAPF_G2RL

    MAPF_G2RL is a Python framework training deep reinforcement learning agents for efficient multi-agent path finding on graphs.
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    What is MAPF_G2RL?
    MAPF_G2RL is an open-source research framework that bridges graph theory and deep reinforcement learning to tackle the multi-agent path finding (MAPF) problem. It encodes nodes and edges into vector representations, defines spatial and collision-aware reward functions, and supports various RL algorithms such as DQN, PPO, and A2C. The framework automates scenario creation by generating random graphs or importing real-world maps, and orchestrates training loops that optimize policies for multiple agents simultaneously. After learning, agents are evaluated in simulated environments to measure path optimality, makespan, and success rates. Its modular design allows researchers to extend core components, integrate new MARL techniques, and benchmark against classical solvers.
    MAPF_G2RL Core Features
    • Graph encoding and preprocessing
    • Customizable reward shaping modules
    • Support for DQN, PPO, A2C algorithms
    • Scenario generator for random and real-world maps
    • Multi-agent training and evaluation pipelines
    • Performance logging and visualization tools

  • MARFT

    MARFT is an open-source multi-agent RL fine-tuning toolkit for collaborative AI workflows and language model optimization.
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    What is MARFT?
    MARFT is a Python-based LLMs, enabling reproducible experiments and rapid prototyping of collaborative AI systems.

  • RL Shooter

    RL Shooter provides a customizable Doom-based reinforcement learning environment for training AI agents to navigate and shoot targets.
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    What is RL Shooter?
    RL Shooter is a Python-based framework that integrates ViZDoom with OpenAI Gym APIs to create a flexible reinforcement learning environment for FPS games. Users can define custom scenarios, maps, and reward structures to train agents on navigation, target detection, and shooting tasks. With configurable observation frames, action spaces, and logging facilities, it supports popular deep RL libraries such as Stable Baselines and RLlib, enabling clear performance tracking and reproducibility across experiments.
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