Summary of Provably Efficient Long-horizon Exploration in Monte Carlo Tree Search Through State Occupancy Regularization, by Liam Schramm et al.
Provably Efficient Long-Horizon Exploration in Monte Carlo Tree Search through State Occupancy Regularization
by Liam Schramm, Abdeslam Boularias
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 Volume-MCTS algorithm combines Monte Carlo tree search with policy optimization and state occupancy measure regularization to improve long-horizon exploration in motion planning. By deriving count-based exploration and sampling-based motion planning as approximate solutions to this regularized objective, the method demonstrates better performance than AlphaZero in robot navigation problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Volume-MCTS is a new way of searching for paths that combines two existing approaches: Monte Carlo tree search and policy optimization. The goal is to make it easier for robots to explore their environment by finding the best path. This algorithm does better than others, like AlphaZero, when looking far ahead. |
Keywords
» Artificial intelligence » Optimization » Regularization
