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Comprehensive funções de recompensa personalizadas Tools for Every Need

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funções de recompensa personalizadas

  • Simple Playgrounds

    A lightweight Python library for creating customizable 2D grid environments to train and test reinforcement learning agents.
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    What is Simple Playgrounds?
    Simple Playgrounds provides a modular platform for building interactive 2D grid environments where agents can navigate mazes, interact with objects, and complete tasks. Users define environment layouts, object behaviors, and reward functions via simple YAML or Python scripts. The integrated Pygame renderer delivers real-time visualization, while a step-based API ensures seamless integration with reinforcement learning libraries like Stable Baselines3. With support for multi-agent setups, collision detection, and customizable physics parameters, Simple Playgrounds streamlines the prototyping, benchmarking, and educational demonstration of AI algorithms.
    Simple Playgrounds Core Features
    • Customizable 2D grid-world environment layouts
    • Scenario scripting via Python or YAML
    • Flexible reward function configuration
    • Real-time Pygame-based rendering
    • Step-based API compatible with RL libraries
    • Multi-agent environment support
    • Collision detection and basic physics

  • StarCraft II Reinforcement Learning Agent

    An open-source reinforcement learning agent using PPO to train and play StarCraft II via DeepMind's PySC2 environment.
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    What is StarCraft II Reinforcement Learning Agent?
    This repository provides an end-to-end reinforcement learning framework for StarCraft II gameplay research. The core agent uses Proximal Policy Optimization (PPO) to learn policy networks that interpret observation data from the PySC2 environment and output precise in-game actions. Developers can configure neural network layers, reward shaping, and training schedules to optimize performance. The system supports multiprocessing for efficient sample collection, logging utilities for monitoring training curves, and evaluation scripts for running trained policies against scripted or built-in AI opponents. The codebase is written in Python and leverages TensorFlow for model definition and optimization. Users can extend components such as custom reward functions, state preprocessing, or network architectures to suit specific research objectives.

  • Multi-Agent Surveillance

    Open-source Python environment for training AI agents to cooperatively surveil and detect intruders in grid-based scenarios.
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    What is Multi-Agent Surveillance?
    Multi-Agent Surveillance offers a flexible simulation framework where multiple AI agents act as predators or evaders in a discrete grid world. Users can configure environment parameters such as grid dimensions, number of agents, detection radii, and reward structures. The repository includes Python classes for agent behavior, scenario generation scripts, built-in visualization via matplotlib, and seamless integration with popular reinforcement learning libraries. This makes it easy to benchmark multi-agent coordination, develop custom surveillance strategies, and conduct reproducible experiments.
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