Prioritized Experience Replay for DQN

描述

贡献者指南

技术栈

pythonpytorch

领域

machine learning

议题类型

feature

难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。

3

预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。

3-5 days

活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。

active

清晰度议题是否清楚说明期望改动、验收标准和下一步。

needs investigation

前置要求

Familiarity with DQNPyTorch basicsStable Baselines3 codebase

新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。

30

研究方向

The issue requests implementing Prioritized Experience Replay (PER) for DQN, as introduced in the paper by Schaul et al. (2015). Start by examining the existing DQN implementation in stable baselines3, located in `stable baselines3/dqn/`. The PER logic should modify the replay buffer to sample transitions with probability proportional to TD error, and adjust the loss accordingly. Reference other implementations like the one in `openai/baselines` or `rl adventure` for guidance. Engaging with the issue comments may reveal community suggestions or potential design decisions.