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RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm
Summary
This technical paper presents RLInspect, an interactive visual analytic tool designed to assist users in understanding and potentially debugging the training process of reinforcement learning (RL) algorithms. RLInspect provides users with a visual representation of various components of RL, such as state, action, agent architecture, and reward, which can help them identify issues during training and ultimately improve the performance of the RL model. The authors provide detailed information on the architecture and functionality of RLInspect, including examples from a Cartpole environment, and discuss potential future improvements and limitations.
原文链接：https://arxiv.org/abs/2411.08392
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