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Found 4 papers, 1 papers with code
Verifiable Reinforcement Learning Systems via Compositionality
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal-Logic Tasks
Control and planning of multi-agent systems is an active and increasingly studied topic of research, with many practical applications such as rescue missions, security, surveillance, and transportation.
Joint Learning of Reward Machines and Policies in Environments with Partially Known Semantics
We develop a reinforcement-learning algorithm that infers a reward machine that encodes the underlying task while learning how to execute it, despite the uncertainties of the propositions' truth values.
Verifiable and Compositional Reinforcement Learning Systems
We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task.
