Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications

This paper addresses the problem of designing optimal control policies for mobile robots with mission and safety requirements specified using Linear Temporal Logic (LTL). We consider robots with unknown stochastic dynamics operating in environments with unknown geometric structure. The robots are equipped with sensors allowing them to detect obstacles. Our goal is to synthesize a control policy that maximizes the probability of satisfying an LTL-encoded task in the presence of motion and environmental uncertainty. Several deep reinforcement learning (DRL) algorithms have been proposed recently to address similar problems. A common limitation in related works is that of slow learning performance. In order to address this issue, we propose a novel DRL algorithm, which has the capability to learn control policies at a notably faster rate compared to similar methods. Its sample efficiency is due to a mission-driven exploration strategy that prioritizes exploration towards directions that may contribute to mission accomplishment. Identifying these directions relies on an automaton representation of the LTL task as well as a learned neural network that (partially) models the unknown system dynamics. We provide comparative experiments demonstrating the efficiency of our algorithm on robot navigation tasks in unknown environments.

Verified Compositional Neuro-Symbolic Control for Stochastic Systems with Temporal Logic Tasks

Several methods have been proposed recently to learn neural network (NN) controllers for autonomous agents, with unknown and stochastic dynamics, tasked with complex missions captured by Linear Temporal Logic (LTL). Due to the sample-inefficiency of the majority of these works, compositional learning methods have been proposed decomposing the LTL specification into smaller sub-tasks. Then, separate controllers are learned and composed to satisfy the original task. A key challenge within these approaches is that they often lack safety guarantees or the provided guarantees are impractical. This paper aims to address this challenge. Particularly, we consider autonomous systems with unknown and stochastic dynamics and LTL-encoded tasks. We assume that the system is equipped with a finite set of base skills modeled by trained NN feedback controllers. Our goal is to check if there exists a temporal composition of the trained NN controllers - and if so, to compute it - that will yield a composite system behavior that satisfies the assigned LTL task with probability one. We propose a new approach that relies on a novel integration of automata theory and data-driven reachability analysis tools for NN-controlled stochastic systems. The resulting neuro-symbolic controller allows the agent to generate safe behaviors for unseen complex temporal logic tasks in a zero-shot fashion by leveraging its base skills. We show correctness of the proposed method and we provide conditions under which it is complete. To the best of our knowledge, this is the first work that designs verified temporal compositions of NN controllers for unknown and stochastic systems. Finally, we provide extensive numerical simulations and hardware experiments on robot navigation tasks to demonstrate the proposed method.