Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2402.03678v3
• Date: Wed, 3 Apr 2024 00:45:12 GMT
• Title: Logical Specifications-guided Dynamic Task Sampling for Reinforcement Learning Agents
• Authors: Yash Shukla, Tanushree Burman, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov,
• Abstract summary: 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.
• Score: 9.529492371336286
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTL$_f$) formulas or Reward Machines (RM), to guide the learning progress of the agent. In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that 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, while minimizing the number of environmental interactions. Unlike previous work, LSTS does not assume information about the environment dynamics or the Reward Machine, and dynamically samples promising tasks that lead to successful goal policies. We evaluate LSTS on a gridworld and show that it achieves improved time-to-threshold performance on complex sequential decision-making problems compared to state-of-the-art RM and Automaton-guided RL baselines, such as Q-Learning for Reward Machines and Compositional RL from logical Specifications (DIRL). Moreover, we demonstrate that our method outperforms RM and Automaton-guided RL baselines in terms of sample-efficiency, both in a partially observable robotic task and in a continuous control robotic manipulation task.

Related papers

• Reinforcement Learning with Action Sequence for Data-Efficient Robot Learning [62.3886343725955]
  We introduce a novel RL algorithm that learns a critic network that outputs Q-values over a sequence of actions. By explicitly training the value functions to learn the consequence of executing a series of current and future actions, our algorithm allows for learning useful value functions from noisy trajectories.
  arXiv  Detail & Related papers  (2024-11-19T01:23:52Z)
• 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)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications [11.812602599752294]
  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.
  arXiv  Detail & Related papers  (2023-11-28T18:59:58Z)
• Exploration via Planning for Information about the Optimal Trajectory [67.33886176127578]
  We develop a method that allows us to plan for exploration while taking the task and the current knowledge into account. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines.
  arXiv  Detail & Related papers  (2022-10-06T20:28:55Z)
• 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)
• Multitask Adaptation by Retrospective Exploration with Learned World Models [77.34726150561087]
  We propose a meta-learned addressing model called RAMa that provides training samples for the MBRL agent taken from task-agnostic storage. The model is trained to maximize the expected agent's performance by selecting promising trajectories solving prior tasks from the storage.
  arXiv  Detail & Related papers  (2021-10-25T20:02:57Z)
• Meta-Reinforcement Learning in Broad and Non-Parametric Environments [8.091658684517103]
  We introduce TIGR, a Task-Inference-based meta-RL algorithm for tasks in non-parametric environments. We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective. We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared to state-of-the-art meta-RL approaches.
  arXiv  Detail & Related papers  (2021-08-08T19:32:44Z)
• Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning [83.66080019570461]
  We propose two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty. We show that these metrics have higher correlations with normalized task solvability scores than a variety of alternatives. These metrics can also be used for fast and compute-efficient optimizations of key design parameters.
  arXiv  Detail & Related papers  (2021-03-23T17:49:50Z)
• Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies [57.27944046925876]
  We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph. Instead of directly learning a meta-policy, we develop a Meta-learner with Subtask Graph Inference. Our experiment results on two grid-world domains and StarCraft II environments show that the proposed method is able to accurately infer the latent task parameter.
  arXiv  Detail & Related papers  (2020-01-01T17:34:00Z)

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.