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 …

Training reinforcement learning (RL) agents using scalar reward signals is often infeasible
when an environment has sparse and non-Markovian rewards. Moreover, handcrafting …

Natural and formal languages provide an effective mechanism for humans to specify
instructions and reward functions. We investigate how to generate policies via RL when …

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 …

A critical challenge in neuro-symbolic (NeSy) approaches is to handle the symbol grounding
problem without direct supervision. That is mapping high-dimensional raw data into an …

Reward Machines provide an automata-inspired structure for specifying instructions, safety
constraints, and other temporally extended reward-worthy behaviour. By exposing complex …

Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents
must consider the entire history of state-action pairs to act rationally in the environment. Most …

This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for
reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM …

Deep learning and symbolic artificial intelligence remain the two main paradigms in Artificial
Intelligence (AI), each presenting their own strengths and weaknesses. Artificial agents …