Model-Based Reinforcement Learning for Environments with Delayed Feedback
- Title
- Model-Based Reinforcement Learning for Environments with Delayed Feedback
- Authors
- Kim, Jangwon; Kim, Hangyeol; Kang, Jiwook; HAN, SOOHEE
- Date Issued
- 2023-10-18
- Publisher
- ICROS
- Abstract
- In recent years, reinforcement learning (RL) has made significant improvements in complex games, continuous
control tasks, and real-world control tasks. However, there are several obstacles to adapting RL to the real world. One
of the difficulties is a signal delay. In real world tasks, signal delay can occur in many cases and is often not avoidable.
The mismatch between delayed observation and true observation causes performance degradation. To overcome this
issue, we propose a Model-Based State Estimation (MBSE) algorithm that estimates the true feedback from the delayed
feedback. We tested our algorithm on MuJoCo control tasks and compared it with other algorithms in a delayed feedback
environment, and our algorithm showed significant performance improvement.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/122409
- Article Type
- Conference
- Citation
- 2023 The 23rd International Conference on Control, Automation and Systems (ICCAS 2023), 2023-10-18
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