Summary of Action Mapping For Reinforcement Learning in Continuous Environments with Constraints, by Mirco Theile et al.
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
by Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 This paper proposes a novel deep reinforcement learning (DRL) strategy called “action mapping” to improve sample efficiency and convergence rates in environments with constraints. The approach leverages feasibility models to streamline the learning process by decoupling the selection of feasible actions from policy optimization. This allows DRL agents to focus on selecting optimal actions from a reduced set of feasible options, leading to improved training performance in constrained environments with continuous action spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by improving how they make decisions when some choices are not allowed. The new method, called “action mapping,” makes it easier for machines to find the best action by breaking down the decision-making process into two steps: finding possible actions and choosing the best one. This makes training faster and more efficient. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
