Multi-Robot Path Planning Combining Heuristics and Multi-Agent Reinforcement Learning

Multi-robot path finding in dynamic environments is a highly challenging classic problem. In the movement process, robots need to avoid collisions with other moving robots while minimizing their travel distance. Previous methods for this problem either continuously replan paths using heuristic search methods to avoid conflicts or choose appropriate collision avoidance strategies based on learning approaches. The former may result in long travel distances due to frequent replanning, while the latter may have low learning efficiency due to low sample exploration and utilization, and causing high training costs for the model. To address these issues, we propose a path planning method, MAPPOHR, which combines heuristic search, empirical rules, and multi-agent reinforcement learning. The method consists of two layers: a real-time planner based on the multi-agent reinforcement learning algorithm, MAPPO, which embeds empirical rules in the action output layer and reward functions, and a heuristic search planner used to create a global guiding path. During movement, the heuristic search planner replans new paths based on the instructions of the real-time planner. We tested our method in 10 different conflict scenarios. The experiments show that the planning performance of MAPPOHR is better than that of existing learning and heuristic methods. Due to the utilization of empirical knowledge and heuristic search, the learning efficiency of MAPPOHR is higher than that of existing learning methods

Automatic Feature Engineering through Monte Carlo Tree Search

The performance of machine learning models depends heavily on the feature space and feature engineering. Although neural networks have made significant progress in learning latent feature spaces from data, compositional feature engineering through nested feature transformations can reduce model complexity and can be particularly desirable for interpretability. To find suitable transformations automatically, state-of-the-art methods model the feature transformation space by graph structures and use heuristics such as $\epsilon$-greedy to search for them. Such search strategies tend to become less efficient over time because they do not consider the sequential information of the candidate sequences and cannot dynamically adjust the heuristic strategy. To address these shortcomings, we propose a reinforcement learning-based automatic feature engineering method, which we call Monte Carlo tree search Automatic Feature Engineering (mCAFE). We employ a surrogate model that can capture the sequential information contained in the transformation sequence and thus can dynamically adjust the exploration strategy. It balances exploration and exploitation by Thompson sampling and uses a Long Short Term Memory (LSTM) based surrogate model to estimate sequences of promising transformations. In our experiments, mCAFE outperformed state-of-the-art automatic feature engineering methods on most common benchmark datasets

Monte Carlo Approaches to Parameterized Poker Squares

The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques