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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
4,005Vezi pe GitHubAcme is a reinforcement learning framework and execution engine designed for developing and benchmarking learning algorithms. It provides a library of modular components and reference implementations used to construct agents and establish performance baselines. The system enables the scaling of agent architectures from single-stream execution to large distributed environments. This allows for the transition from initial prototyping to distributed execution for training and evaluation. The framework covers reinforcement learning development and agent architecture prototyping, providing the bu
Implements toolkits for benchmarking new reinforcement learning algorithms against standard reference agents.
Python - datamllab/rlcard
datamllab/rlcard
3,401Vezi pe GitHubRLcard 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
2,725Vezi pe GitHubThis 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
