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

• URL: http://arxiv.org/abs/2311.17059v1
• Date: Tue, 28 Nov 2023 18:59:58 GMT
• Title: Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications
• Authors: Jun Wang, Hosein Hasanbeig, Kaiyuan Tan, Zihe Sun, Yiannis Kantaros
• Abstract summary: We consider robots with unknown dynamics operating in environments with unknown structure. Our goal is to synthesize a control policy that maximizes the probability of satisfying an automaton-encoded task. We propose a novel DRL algorithm, which has the capability to learn control policies at a notably faster rate compared to similar methods.
• Score: 11.812602599752294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration [13.053013407015628]
  This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics. We propose an accelerated RL algorithm that can learn control policies significantly faster than competitive approaches.
  arXiv  Detail & Related papers  (2024-10-16T00:53:41Z)
• Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents [9.529492371336286]
  Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. We propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS) LSTS learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification.
  arXiv  Detail & Related papers  (2024-02-06T04:00:21Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• Comparing Active Learning Performance Driven by Gaussian Processes or Bayesian Neural Networks for Constrained Trajectory Exploration [0.0]
  Currently, humans drive robots to meet scientific objectives, but depending on the robot's location, the exchange of information and driving commands may cause undue delays in mission fulfillment. An autonomous robot encoded with a scientific objective and an exploration strategy incurs no communication delays and can fulfill missions more quickly. Active learning algorithms offer this capability of intelligent exploration, but the underlying model structure varies the performance of the active learning algorithm in accurately forming an understanding of the environment.
  arXiv  Detail & Related papers  (2023-09-28T02:45:14Z)
• Leveraging Sequentiality in Reinforcement Learning from a Single Demonstration [68.94506047556412]
  We propose to leverage a sequential bias to learn control policies for complex robotic tasks using a single demonstration. We show that DCIL-II can solve with unprecedented sample efficiency some challenging simulated tasks such as humanoid locomotion and stand-up.
  arXiv  Detail & Related papers  (2022-11-09T10:28:40Z)
• Verifying Learning-Based Robotic Navigation Systems [61.01217374879221]
  We show how modern verification engines can be used for effective model selection. Specifically, we use verification to detect and rule out policies that may demonstrate suboptimal behavior. Our work is the first to demonstrate the use of verification backends for recognizing suboptimal DRL policies in real-world robots.
  arXiv  Detail & Related papers  (2022-05-26T17:56:43Z)
• Accelerated Reinforcement Learning for Temporal Logic Control Objectives [10.216293366496688]
  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.
  arXiv  Detail & Related papers  (2022-05-09T17:09:51Z)
• Overcoming Exploration: Deep Reinforcement Learning in Complex Environments from Temporal Logic Specifications [2.8904578737516764]
  We present a Deep Reinforcement Learning (DRL) algorithm for a task-guided robot with unknown continuous-time dynamics deployed in a large-scale complex environment. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale complex environments.
  arXiv  Detail & Related papers  (2022-01-28T16:39:08Z)
• Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives [92.0321404272942]
  Reinforcement learning can be used to build general-purpose robotic systems. However, training RL agents to solve robotics tasks still remains challenging. In this work, we manually specify a library of robot action primitives (RAPS), parameterized with arguments that are learned by an RL policy. We find that our simple change to the action interface substantially improves both the learning efficiency and task performance.
  arXiv  Detail & Related papers  (2021-10-28T17:59:30Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Learning to Track Dynamic Targets in Partially Known Environments [48.49957897251128]
  We use a deep reinforcement learning approach to solve active target tracking. In particular, we introduce Active Tracking Target Network (ATTN), a unified RL policy that is capable of solving major sub-tasks of active target tracking.
  arXiv  Detail & Related papers  (2020-06-17T22:45:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.