Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

• URL: http://arxiv.org/abs/2306.13732v1
• Date: Fri, 23 Jun 2023 18:42:27 GMT
• Title: Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
• Authors: Yash Paliwal, Rajarshi Roy, Jean-Rapha\"el Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu, Zhe Xu
• Abstract summary: We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. While these machines model the reward function, they often overlook the causal knowledge about the environment. We propose the Temporal-Logic-based Causal Diagram (TL-CD) in RL, which captures the temporal causal relationships between different properties of the environment.
• Score: 25.538860320318943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them into the state-space for RL algorithms. However, while these machines model the reward function, they often overlook the causal knowledge about the environment. To address this limitation, we propose the Temporal-Logic-based Causal Diagram (TL-CD) in RL, which captures the temporal causal relationships between different properties of the environment. We exploit the TL-CD to devise an RL algorithm in which an agent requires significantly less exploration of the environment. To this end, based on a TL-CD and a task DFA, we identify configurations where the agent can determine the expected rewards early during an exploration. Through a series of case studies, we demonstrate the benefits of using TL-CDs, particularly the faster convergence of the algorithm to an optimal policy due to reduced exploration of the environment.

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