Learning Task Automata for Reinforcement Learning using Hidden Markov Models

• URL: http://arxiv.org/abs/2208.11838v4
• Date: Tue, 3 Oct 2023 16:46:16 GMT
• Title: Learning Task Automata for Reinforcement Learning using Hidden Markov Models
• Authors: Alessandro Abate (1), Yousif Almulla (2), James Fox (1), David Hyland (1), Michael Wooldridge (1) ((1) University of Oxford, (2) Microsoft Azure Quantum)
• Abstract summary: This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state task automata' We learn a product MDP, a model composed of the specification's automaton and the environment's MDP, by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy.
• Score: 37.69303106863453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training reinforcement learning (RL) agents using scalar reward signals is often infeasible when an environment has sparse and non-Markovian rewards. Moreover, handcrafting these reward functions before training is prone to misspecification, especially when the environment's dynamics are only partially known. This paper proposes a novel pipeline for learning non-Markovian task specifications as succinct finite-state `task automata' from episodes of agent experience within unknown environments. We leverage two key algorithmic insights. First, we learn a product MDP, a model composed of the specification's automaton and the environment's MDP (both initially unknown), by treating the product MDP as a partially observable MDP and using the well-known Baum-Welch algorithm for learning hidden Markov models. Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP. Our learnt task automaton enables the decomposition of a task into its constituent sub-tasks, which improves the rate at which an RL agent can later synthesise an optimal policy. It also provides an interpretable encoding of high-level environmental and task features, so a human can readily verify that the agent has learnt coherent tasks with no misspecifications. In addition, we take steps towards ensuring that the learnt automaton is environment-agnostic, making it well-suited for use in transfer learning. Finally, we provide experimental results compared with two baselines to illustrate our algorithm's performance in different environments and tasks.

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