Decentralised Monte Carlo Tree Search for Active Perception

Best, Graeme; Cliff, Oliver M.; Patten, Timothy; Mettu, Ramgopal R.

doi:10.1007/978-3-030-43089-4_55

Cited by 22 publications

(17 citation statements)

References 21 publications

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“…This paper is an extended and revised version of Best et al (2016) presented at WAFR 2016. The main new contribution is a theoretical analysis of the key MCTS component of our algorithm by relating it to a new multi-armed bandit (MAB) problem.…”

Section: Introductionmentioning

confidence: 99%

Dec-MCTS: Decentralized planning for multi-robot active perception

Best

Cliff

Patten

et al. 2018

The International Journal of Robotics Research

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153

138

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We propose a decentralized variant of Monte Carlo tree search (MCTS) that is suitable for a variety of tasks in multi-robot active perception. Our algorithm allows each robot to optimize its own actions by maintaining a probability distribution over plans in the joint-action space. Robots periodically communicate a compressed form of their search trees, which are used to update the joint distribution using a distributed optimization approach inspired by variational methods. Our method admits any objective function defined over robot action sequences, assumes intermittent communication, is anytime, and is suitable for online replanning. Our algorithm features a new MCTS tree expansion policy that is designed for our planning scenario. We extend the theoretical analysis of standard MCTS to provide guarantees for convergence rates to the optimal payoff sequence. We evaluate the performance of our method for generalized team orienteering and online active object recognition using real data, and show that it compares favorably to centralized MCTS even with severely degraded communication. These examples demonstrate the suitability of our algorithm for real-world active perception with multiple robots.

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Section: Introductionmentioning

confidence: 99%

Dec-MCTS: Decentralized planning for multi-robot active perception

Best

Cliff

Patten

et al. 2018

The International Journal of Robotics Research

Self Cite

153

138

View full text Add to dashboard Cite

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“…In this paper, we study this issue in the context of decentralised Monte Carlo tree search (Dec-MCTS) [1], [12], a recently proposed algorithm for asynchronous multi-robot coordination. Dec-MCTS is applicable to a general class of problem formulations and has been shown to work particularly well at multi-robot information gathering scenarios.…”

Section: Introductionmentioning

confidence: 99%

Planning-Aware Communication for Decentralised Multi-Robot Coordination

Best

Forrai²,

Mettu

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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We present an algorithm for selecting when to communicate during online planning phases of coordinated multi-robot missions. The key idea is that a robot decides to request communication from another robot by reasoning over the predicted information value of communication messages over a sliding time-horizon, where communication messages are probability distributions over action sequences. We formulate this problem in the context of the recently proposed decentralised Monte Carlo tree search (Dec-MCTS) algorithm for online, decentralised multi-robot coordination. We propose a particle filter for predicting the information value, and a polynomialtime belief-space planning algorithm for finding the optimal communication schedules in an online and decentralised manner. We evaluate the benefit of informative communication planning for a multi-robot information gathering scenario with 8 simulated robots. Our results show reductions in channel utilisation of up to four-fifths with surprisingly little impact on coordination performance.

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“…The authors however assume heuristic (so not always accurate) models of other agents and do not learn their actual policies. [Best et al, 2016] uses parallel MCTS for an active perception task and combines it with communication to solve the coordination problem; [Li et al, 2019] explores similar ideas and combines communication with heuristic teammate models of [Claes et al, 2017]. Contrary to both of these papers, we assume no communication during the execution phase.…”

Section: Related Workmentioning

confidence: 99%

Decentralized MCTS via Learned Teammate Models

Czechowski

Oliehoek

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key difficulty of such approach lies in making accurate predictions about the decisions of other agents. In this paper, we present a trainable online decentralized planning algorithm based on decentralized Monte Carlo Tree Search, combined with models of teammates learned from previous episodic runs. By only allowing one agent to adapt its models at a time, under the assumption of ideal policy approximation, successive iterations of our method are guaranteed to improve joint policies, and eventually lead to convergence to a Nash equilibrium. We test the efficiency of the algorithm by performing experiments in several scenarios of the spatial task allocation environment introduced in [Claes et al., 2015]. We show that deep learning and convolutional neural networks can be employed to produce accurate policy approximators which exploit the spatial features of the problem, and that the proposed algorithm improves over the baseline planning performance for particularly challenging domain configurations.

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Decentralised Monte Carlo Tree Search for Active Perception

Cited by 22 publications

References 21 publications

Dec-MCTS: Decentralized planning for multi-robot active perception

Dec-MCTS: Decentralized planning for multi-robot active perception

Planning-Aware Communication for Decentralised Multi-Robot Coordination

Decentralized MCTS via Learned Teammate Models

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