Summary of Truncating Trajectories in Monte Carlo Policy Evaluation: An Adaptive Approach, by Riccardo Poiani et al.
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
by Riccardo Poiani, Nicole Nobili, Alberto Maria Metelli, Marcello Restelli
First submitted to arxiv on: 17 Oct 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 Policy evaluation in Monte Carlo (MC) Reinforcement Learning (RL) algorithms, such as policy gradient methods, typically involves specifying an interaction budget for collecting trajectories within a simulator. This paper proposes a surrogate index for evaluating mean squared error of return estimators using truncated trajectories. The findings suggest that fixed-length trajectory schedules are suboptimal and that adaptive data collection strategies can allocate more transitions in timesteps requiring more accurate sampling to reduce estimation errors. Building on these results, the authors introduce Robust and Iterative Data Collection Strategy Optimization (RIDO), an algorithm splitting the available interaction budget into mini-batches, minimizing an empirical and robust version of the surrogate error at each round. RIDO’s performance is assessed across multiple domains, demonstrating its ability to adapt trajectory schedules for improved estimation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers learn by making decisions based on experience. It focuses on a specific way that computers evaluate their decisions, called Monte Carlo simulation. The researchers found that the current method of collecting data is not the best and propose a new approach to make better decisions. This new approach, called RIDO, adjusts its strategy as it learns, giving more attention to situations where it needs more information to make accurate decisions. The results show that RIDO can improve the accuracy of its decisions across different scenarios. |
Keywords
* Artificial intelligence * Attention * Optimization * Reinforcement learning
