Knowledge Commons of Institute of Automation,CAS
Continuous Exploration via Multiple Perspectives in Sparse Reward Environment | |
Chen ZP(陈忠鹏)1,2![]() ![]() | |
2023-12 | |
会议名称 | 第六届中国模式识别与计算机视觉大会(PRCV 2023) |
会议日期 | 2023-10-13 |
会议地点 | 厦门国际会议中心 |
摘要 | Exploration is a major challenge in deep reinforcement learning, especially in cases where reward is sparse. Simple random exploration strategies, such as epsilon-greedy, struggle to solve the hard exploration problem in the sparse reward environment. A more effective approach to solve the hard exploration problem in the sparse reward environment is to use an exploration strategy based on intrinsic motivation, where the key point is to design reasonable and effective intrinsic reward to drive the agent to explore. This paper proposes a method called CEMP, which drives the agent to explore more effectively and continuously in the sparse reward environment. CEMP contributes a new framework for designing intrinsic reward from multiple perspectives, and can be easily integrated into various existing reinforcement learning algorithms. In addition, experimental results in a series of complex and sparse reward environments in MiniGrid demonstrate that our proposed CEMP method achieves better final performance and faster learning efficiency than ICM, RIDE, and TRPO-AE-Hash, which only calculate intrinsic reward from a single perspective. |
关键词 | Reinforcement Learning · Exploration Strategy · Sparse Reward · Intrinsic Motivation |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 强化与进化学习 |
国重实验室规划方向分类 | 复杂系统建模与推演 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57193 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Guan Q(关强) |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen ZP,Guan Q. Continuous Exploration via Multiple Perspectives in Sparse Reward Environment[C],2023. |
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论文最终出版版本.pdf(2260KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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