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Comprehensive Benchmarking von Algorithmen Tools for Every Need

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Benchmarking von Algorithmen

  • 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.
    RL Shooter Core Features
    • Customizable FPS scenarios on ViZDoom
    • Visual frame observation streams
    • Configurable reward functions
    • Adjustable action space definitions
    • OpenAI Gym API compatibility
    • Frame skipping and FPS control
    • Logging and TensorBoard support

  • DeepMind MAS Environment

    Gym-compatible multi-agent reinforcement learning environment offering customizable scenarios, rewards, and agent communication.
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    What is DeepMind MAS Environment?
    DeepMind MAS Environment is a Python library that provides a standardized interface for building and simulating multi-agent reinforcement learning tasks. It allows users to configure number of agents, define observation and action spaces, and customize reward structures. The framework supports agent-to-agent communication channels, performance logging, and rendering capabilities. Researchers can seamlessly integrate DeepMind MAS Environment with popular RL libraries such as TensorFlow and PyTorch to benchmark new algorithms, test communication protocols, and analyze both discrete and continuous control domains.
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