Summary of Act As You Learn: Adaptive Decision-making in Non-stationary Markov Decision Processes, by Baiting Luo et al.
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
by Baiting Luo, Yunuo Zhang, Abhishek Dubey, Ayan Mukhopadhyay
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A novel approach to sequential decision-making in non-stationary environments is presented, addressing two major shortcomings of existing methods. The proposed heuristic search algorithm, Adaptive Monte Carlo Tree Search (ADA-MCTS), learns the updated dynamics of the environment over time and adapts its behavior accordingly. By disentangling aleatoric and epistemic uncertainty in the agent’s updated belief, ADA-MCTS enables more informed decision-making. Experimental results demonstrate that this approach outperforms state-of-the-art methods across multiple open-source problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make decisions when things change over time is introduced. The problem with current methods is that they assume we know what will happen next, which isn’t always the case. They also tend to be overly cautious, but this algorithm learns and adapts as it goes along. It does this by understanding how much we don’t know and using that information to make better decisions. |
