Summary of A Dual Perspective Of Reinforcement Learning For Imposing Policy Constraints, by Bram De Cooman and Johan Suykens
A Dual Perspective of Reinforcement Learning for Imposing Policy Constraints
by Bram De Cooman, Johan Suykens
First submitted to arxiv on: 25 Apr 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 unifies existing techniques for model-free reinforcement learning methods to impose behavioral constraints on trained policies. By bridging classical optimization and control theory with value-based and actor-critic reinforcement learning, the authors develop a generic primal-dual framework that enables the imposition of various constraints on learned policies. The resulting algorithm, DualCRL, allows designers to automatically handle different combinations of policy constraints during training using trainable reward modifications. Evaluations in two interpretable environments demonstrate the efficacy of this versatile toolbox for designing systems with specific behavioral constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn from experience without needing a blueprint. It makes sure the computer behaves well by setting rules, like not taking too many actions or staying within certain boundaries. The authors combine different techniques to create a new way to make these rules work together seamlessly. This allows designers to easily add specific rules during training and test it in different situations. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
