Reward machines: Exploiting reward function structure in reinforcement learning
Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …
such, these methods must extensively interact with the environment in order to discover …
Learning reward machines for partially observable reinforcement learning
Abstract Reward Machines (RMs), originally proposed for specifying problems in
Reinforcement Learning (RL), provide a structured, automata-based representation of a …
Reinforcement Learning (RL), provide a structured, automata-based representation of a …
Interpretable sequence classification via discrete optimization
Sequence classification is the task of predicting a class label given a sequence of
observations. In many applications such as healthcare monitoring or intrusion detection …
observations. In many applications such as healthcare monitoring or intrusion detection …
Learning non-Markovian reward models in MDPs
There are situations in which an agent should receive rewards only after having
accomplished a series of previous tasks. In other words, the reward that the agent receives …
accomplished a series of previous tasks. In other words, the reward that the agent receives …
Reward Machines
RAT Icarte - 2022 - search.proquest.com
Reinforcement learning involves the study of how to solve sequential decision-making
problems using minimal supervision or prior knowledge. In contrast to most methods for …
problems using minimal supervision or prior knowledge. In contrast to most methods for …
Reward Machines
RA Toro Icarte - 2022 - tspace.library.utoronto.ca
Reinforcement learning involves the study of how to solve sequential decision-making
problems using minimal supervision or prior knowledge. In contrast to most methods for …
problems using minimal supervision or prior knowledge. In contrast to most methods for …