3 مستودعات
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
4,005عرض على GitHubAcme هو إطار عمل ومحرك تنفيذ للتعلم التعزيزي (Reinforcement Learning)، مصمم لتطوير وقياس أداء خوارزميات التعلم. يوفر مكتبة من المكونات النمطية وتطبيقات مرجعية تُستخدم لبناء الوكلاء (Agents) وتحديد معايير الأداء. يُمكّن النظام من توسيع نطاق معماريات الوكلاء من التنفيذ أحادي المسار إلى بيئات موزعة واسعة النطاق. وهذا يسمح بالانتقال من مرحلة النماذج الأولية إلى التنفيذ الموزع للتدريب والتقييم. يغطي إطار العمل تطوير التعلم التعزيزي ونمذجة معماريات الوكلاء، موفراً اللبنات الأساسية اللازمة لمقارنة النماذج الجديدة مقابل وكلاء مرجعيين قياسيين.
Implements toolkits for benchmarking new reinforcement learning algorithms against standard reference agents.
Python - datamllab/rlcard
datamllab/rlcard
3,401عرض على 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
2,725عرض على 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
