Reward machines (RMs) are a recent formalism for representing the reward function of a
reinforcement learning task through a finite-state machine whose edges encode subgoals of …

In reinforcement learning, an agent incrementally refines a behavioral policy through a
series of episodic interactions with its environment. This process can be characterized as …

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in
which high-level knowledge in the form of reward machines is available to the learner …

Improving sample efficiency is central to Reinforcement Learning (RL), especially in
environments where the rewards are sparse. Some recent approaches have proposed to …

Abstract The Belief-Desire-Intention (BDI) approach to agent development has formed the
basis for much of the research on architectures for autonomous agents. A key advantage of …

Research on autonomous agents has long been concerned with reasoning about an agent's
actions within an environment [35], regardless of the underlying agent architecture [15] …

This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that
combines cooperative task decomposition with the learning of Reward Machines (RMs) …

For an agent in a multi-agent environment, it is often beneficial to be able to predict what
other agents will do next when deciding how to act. Previous work in multi-agent intention …

Intelligent agents, like those based on the popular BDI agent paradigm, typically pursue
multiple goals in parallel. An intention scheduler is required to reason about the possible …

BDI (Belief-Desire-Intention) agents represent a paradigm in artificial intelligence,
demonstrating proficiency in reasoning, planning, and decision-making. They offer a …