1 रिपॉजिटरी
Awesome GitHub RepositoriesAgent Action Space Exploration
Strategies for navigating complex operational environments to find high-reward actions.
Distinct from Action-Space Discretization: Focuses on search strategies like MCTS for action selection, not discretization or game simulations.
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Awesome Agent Action Space Exploration GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- openmanus/openmanus-rl
OpenManus/OpenManus-RL
3,916GitHub पर देखेंOpenManus-RL is a reinforcement learning framework and distributed training pipeline designed to train large language models as agents. It serves as an agentic reasoning optimizer and reward model trainer, providing the infrastructure to improve model decision-making through reward-based policy optimization. The project distinguishes itself through a distributed architecture that supports parameter sharding across multiple compute nodes and a coordinated rollout system for collecting interaction trajectories. It incorporates advanced reasoning strategies, such as Tree-of-Thoughts and Monte Ca
Uses search strategies like Monte Carlo Tree Search to navigate large operational environments and optimize action selection.
Python
