Accelerated Reinforcement Learning for Temporal Logic Control Objectives
- URL: http://arxiv.org/abs/2205.04424v2
- Date: Tue, 10 May 2022 21:27:22 GMT
- Title: Accelerated Reinforcement Learning for Temporal Logic Control Objectives
- Authors: Yiannis Kantaros
- Abstract summary: This paper addresses the problem of learning control policies for mobile robots modeled as unknown Markov Decision Processes (MDPs)
We propose a novel accelerated model-based reinforcement learning (RL) algorithm for control objectives that is capable of learning control policies significantly faster than related approaches.
- Score: 10.216293366496688
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper addresses the problem of learning control policies for mobile
robots modeled as unknown Markov Decision Processes (MDPs) that are tasked with
temporal logic missions, such as sequencing, coverage, or surveillance. The MDP
captures uncertainty in the workspace structure and the outcomes of control
decisions. The control objective is to synthesize a control policy that
maximizes the probability of accomplishing a high-level task, specified as a
Linear Temporal Logic (LTL) formula. To address this problem, we propose a
novel accelerated model-based reinforcement learning (RL) algorithm for LTL
control objectives that is capable of learning control policies significantly
faster than related approaches. Its sample-efficiency relies on biasing
exploration towards directions that may contribute to task satisfaction. This
is accomplished by leveraging an automaton representation of the LTL task as
well as a continuously learned MDP model. Finally, we provide extensive
comparative experiments that demonstrate the sample efficiency of the proposed
method against recent temporal logic RL methods.
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