Summary of Periodic Agent-state Based Q-learning For Pomdps, by Amit Sinha et al.
Periodic agent-state based Q-learning for POMDPs
by Amit Sinha, Matthieu Geist, Aditya Mahajan
First submitted to arxiv on: 8 Jul 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 abstract discusses Partially Observable Markov Decision Processes (POMDPs), which are typically converted to fully observed belief-state MDPs. However, this approach is not suitable for reinforcement learning settings. An alternative agent state can be used, which is a model-free, recursively updateable function of the observation history. This allows standard RL algorithms to be adapted to POMDPs, but they learn stationary policies. The authors argue that non-stationary agent-state based policies can outperform stationary ones due to the periodic nature of the agent state. They propose PASQL (periodic agent-state based Q-learning), a variant of agent-state-based Q-learning that learns periodic policies. The paper rigorously establishes that PASQL converges to a cyclic limit and characterizes the approximation error of the converged periodic policy. Finally, a numerical experiment demonstrates the benefit of learning periodic policies over stationary policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary POMDPs are a type of complex problem where machines need to make decisions based on incomplete information. In the past, people have found ways to solve these problems by converting them into simpler ones that computers can handle easily. However, this approach doesn’t work well for another important area called reinforcement learning. The solution is to use something called an agent state, which is like a memory that helps the machine make decisions based on what it has seen before. This allows machines to learn new things and adapt to changing situations. The authors of this paper want to know if they can do even better by using periodic policies, which are like repeating patterns. They propose a new way called PASQL that can learn these patterns and show that it is more effective than the old way. |
Keywords
* Artificial intelligence * Reinforcement learning
