Summary of Reward-punishment Reinforcement Learning with Maximum Entropy, by Jiexin Wang and Eiji Uchibe
Reward-Punishment Reinforcement Learning with Maximum Entropy
by Jiexin Wang, Eiji Uchibe
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The soft Deep MaxPain (softDMP) algorithm is a reinforcement learning approach that integrates long-term policy entropy optimization into reward-punishment objectives to enhance sample efficiency and robustness. Building upon the previous Deep MaxPain method, softDMP addresses two unresolved issues: the collaboration between the negated pain-seeking sub-policy and the “min” operator, and the data collection challenge for learning the punishment module. SoftDMP uses a probabilistic classifier to assign roll-outs to separate replay buffers for updating reward and punishment action-value functions. This approach is evaluated in Turtlebot 3’s maze navigation tasks under the ROS Gazebo simulation, demonstrating superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces an new algorithm called soft Deep MaxPain (softDMP) that helps computers learn from mistakes more efficiently. It does this by changing how it updates its action values based on rewards and punishments. The goal is to make the learning process smoother and more robust. The researchers also fix two problems with previous versions of the algorithm. They test their new approach in a simulation called Turtlebot 3’s maze navigation, where robots have to navigate through mazes. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
