While this method provides constant bias-variance properties at any time step, it often necessitates truncated roll-outs with shorter horizons for faster learning and policy updates within a single ...

An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & vLLM & RFT & Dynamic Sampling & Async Agent RL) ...

The Proximal Policy Optimization (PPO) algorithm is used as the RL agent in this multi-agent framework, where each PPO agent independently manages the SOC of a corresponding battery cell based on ...

Goal-reaching simulation in Unity by combining to use ML-Agents toolkit and Anaconda involves training an agent to navigate and interact with environments to reach predefined goal target. This task ...