3 个仓库
Awesome GitHub RepositoriesRL Algorithm Benchmarking Toolkits
Toolkits for benchmarking reinforcement learning algorithms like DQN, NFSP, and CFR across standardized tasks.
Distinct from Algorithm Benchmarking Libraries: Distinct from Algorithm Benchmarking Libraries: focuses on RL-specific algorithms and standardized card game tasks, not general algorithm benchmarking.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · RL Algorithm Benchmarking Toolkits. Refine with filters or upvote what's useful.
Awesome RL Algorithm Benchmarking Toolkits GitHub Repositories
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- google-deepmind/acme
google-deepmind/acme
4,005在 GitHub 上查看Acme 是一个强化学习框架和执行引擎，旨在开发和基准测试学习算法。它提供了一个模块化组件库和参考实现，用于构建智能体并建立性能基准。 该系统支持将智能体架构从单流执行扩展到大规模分布式环境。这使得从初步原型设计到用于训练和评估的分布式执行的过渡变得更加顺畅。 该框架涵盖了强化学习开发和智能体架构原型设计，提供了将新模型与标准参考智能体进行基准测试所需的构建模块。
Implements toolkits for benchmarking new reinforcement learning algorithms against standard reference agents.
Python - datamllab/rlcard
datamllab/rlcard
3,401在 GitHub 上查看RLcard is an open-source framework for developing and evaluating reinforcement learning agents across multiple card game environments. It functions as a card game environment simulator, a multi-agent RL platform, and a benchmarking toolkit for algorithms like DQN, NFSP, and CFR. The framework provides a game-agnostic environment interface that decouples agent logic from game mechanics, allowing any policy to interact through a common API. It supports pluggable reinforcement learning algorithms that operate on this interface without modifying game logic, and includes a self-play training loop
A toolkit for benchmarking reinforcement learning algorithms like DQN, NFSP, and CFR across standardized card game tasks.
Pythonaiblackjackcard-game - dlr-rm/rl-baselines3-zoo
DLR-RM/rl-baselines3-zoo
2,725在 GitHub 上查看This project is a collection of pretrained reinforcement learning agents and training scripts built on Stable Baselines3 and Gymnasium. It provides a framework for training agents to solve specific tasks, managing experiment reproducibility, and deploying pretrained models. The system includes a specialized benchmarking suite and optimization tools for tuning agent settings. It utilizes automated search spaces and distributed trials to maximize performance, while employing bootstrap sampling to generate statistically robust performance metrics and confidence intervals. Broad capabilities cov
Ships a specialized benchmarking suite for evaluating RL agent success using statistically robust metrics.
Pythondeep-reinforcement-learninggymhyperparameter-optimization
