Summary of Policy Zooming: Adaptive Discretization-based Infinite-horizon Average-reward Reinforcement Learning, by Avik Kar and Rahul Singh
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning
by Avik Kar, Rahul Singh
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 algorithms “zoom” into promising regions of the policy space, achieving adaptivity gains in Lipschitz MDPs for infinite-horizon average-reward reinforcement learning. The upper bound on regret is O(T(1-d_eff-1)), where d_eff depends on model-free and model-based algorithms PZRL-MF and PZRL-MB, respectively. The zooming dimension d_^Phi_z plays a crucial role in capturing adaptivity gains for infinite-horizon average-reward RL. Key findings include the ability to obtain low regret when competing against a low-complexity Phi with small d_^Phi_z and the scaling of d_eff with the dimension of the finite-dimensional policy space under mild technical conditions. Simulation experiments validate the gains arising due to adaptivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers developed new algorithms for infinite-horizon average-reward reinforcement learning. The goal was to make these algorithms more efficient by “zooming” in on good areas of the policy space. They found that this approach can reduce regret and improve performance. The results are important because they show how to make these algorithms work better for complex problems. |
Keywords
» Artificial intelligence » Reinforcement learning
