Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logic

TL;DR

This paper introduces a modular deep reinforcement learning framework that uses temporal logic and an embedded product MDP to improve continuous motion planning for autonomous systems, ensuring high-level task satisfaction.

Contribution

It proposes a novel embedded product MDP with reward shaping based on temporal logic, enabling model-free RL to effectively satisfy complex high-level tasks in continuous spaces.

Findings

The framework guarantees maximization of task satisfaction probability.

The modular DDPG effectively handles continuous state and action spaces.

Experimental results demonstrate improved performance in OpenAI gym environments.

Abstract

This paper investigates the motion planning of autonomous dynamical systems modeled by Markov decision processes (MDP) with unknown transition probabilities over continuous state and action spaces. Linear temporal logic (LTL) is used to specify high-level tasks over infinite horizon, which can be converted into a limit deterministic generalized B\"uchi automaton (LDGBA) with several accepting sets. The novelty is to design an embedded product MDP (EP-MDP) between the LDGBA and the MDP by incorporating a synchronous tracking-frontier function to record unvisited accepting sets of the automaton, and to facilitate the satisfaction of the accepting conditions. The proposed LDGBA-based reward shaping and discounting schemes for the model-free reinforcement learning (RL) only depend on the EP-MDP states and can overcome the issues of sparse rewards. Rigorous analysis shows that any RL method that optimizes the expected discounted return is guaranteed to find an optimal policy whose traces maximize the satisfaction probability. A modular deep deterministic policy gradient (DDPG) is then developed to generate such policies over continuous state and action spaces. The performance of our framework is evaluated via an array of OpenAI gym environments.