Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2304.05271v1
• Date: Tue, 11 Apr 2023 15:14:31 GMT
• Title: Automaton-Guided Curriculum Generation for Reinforcement Learning Agents
• Authors: Yash Shukla, Abhishek Kulkarni, Robert Wright, Alvaro Velasquez, Jivko Sinapov
• Abstract summary: Automaton-guided Curriculum Learning (AGCL) is a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs) AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP representation to generate a curriculum as a DAG. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance.
• Score: 14.20447398253189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a series of correct actions to reach the goal state, considering future transitions while choosing an action). Employing a curriculum (a sequence of increasingly complex tasks) further improves the learning speed of the agent by sequencing intermediate tasks suited to the learning capacity of the agent. However, generating curricula from the logical specification still remains an unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum Learning, a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP (OOMDP) representation to generate a curriculum as a DAG, where the vertices correspond to tasks, and edges correspond to the direction of knowledge transfer. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e.g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e.g, Q-Learning for Reward Machines). Further, we demonstrate that AGCL performs well even in the presence of noise in the task's OOMDP description, and also when distractor objects are present that are not modeled in the logical specification of the tasks' objectives.

Related papers

• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• 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)
• 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)
• CQM: Curriculum Reinforcement Learning with a Quantized World Model [30.21954044028645]
  We propose a novel curriculum method that automatically defines the semantic goal space which contains vital information for the curriculum process. Ours suggests uncertainty and temporal distance-aware curriculum goals that converge to the final goals over the automatically composed goal space. Also, ours outperforms the state-of-the-art curriculum RL methods on data efficiency and performance, in various goal-reaching tasks even with ego-centric visual inputs.
  arXiv  Detail & Related papers  (2023-10-26T11:50:58Z)
• Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning [56.26889258704261]
  We propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA) SAMA prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods.
  arXiv  Detail & Related papers  (2023-05-18T10:37:54Z)
• Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism [120.1998866178014]
  We present a flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTingAnism (ROSETTA) Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. Experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-06T07:31:28Z)
• Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata [18.972270182221262]
  This paper presents a method for learning logical task specifications and cost functions from demonstrations. We employ a spectral learning approach to extract a weighted finite automaton (WFA), approximating the unknown logic structure of the task. We define a product between the WFA for high-level task guidance and a Labeled Markov decision process (L-MDP) for low-level control and optimize a cost function that matches the demonstrator's behavior.
  arXiv  Detail & Related papers  (2021-03-10T06:42:10Z)
• Induction and Exploitation of Subgoal Automata for Reinforcement Learning [75.55324974788475]
  We present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton whose edges are labeled by the task's subgoals. A subgoal automaton also consists of two special states: a state indicating the successful completion of the task, and a state indicating that the task has finished without succeeding.
  arXiv  Detail & Related papers  (2020-09-08T16:42:55Z)
• Adaptive Procedural Task Generation for Hard-Exploration Problems [78.20918366839399]
  We introduce Adaptive Procedural Task Generation (APT-Gen) to facilitate reinforcement learning in hard-exploration problems. At the heart of our approach is a task generator that learns to create tasks from a parameterized task space via a black-box procedural generation module. To enable curriculum learning in the absence of a direct indicator of learning progress, we propose to train the task generator by balancing the agent's performance in the generated tasks and the similarity to the target tasks.
  arXiv  Detail & Related papers  (2020-07-01T09:38:51Z)

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.