Summary of Excluding the Irrelevant: Focusing Reinforcement Learning Through Continuous Action Masking, by Roland Stolz et al.
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking
by Roland Stolz, Hanna Krasowski, Jakob Thumm, Michael Eichelbeck, Philipp Gassert, Matthias Althoff
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The proposed paper introduces three continuous action masking methods for reinforcement learning (RL) to improve training efficiency and effectiveness by focusing learning on the set of relevant actions. By mapping the global action space to state-dependent sets of relevant actions, the methods ensure only relevant actions are executed, enhancing the predictability of the RL agent. The authors use proximal policy optimization (PPO) to evaluate their methods on four control tasks, achieving higher final rewards and faster convergence than the baseline without action masking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new approach in reinforcement learning that can improve training efficiency and effectiveness by focusing on relevant actions. It introduces three continuous action masking methods that map the global action space to state-dependent sets of relevant actions. This ensures that only relevant actions are executed, making the RL agent more predictable. The authors test their methods using PPO on four control tasks and find that they achieve higher final rewards and faster convergence than the baseline. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
