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Explicable reward design for reinforcement learning agents
We study the design of explicable reward functions for a reinforcement learning agent while
guaranteeing that an optimal policy induced by the function belongs to a set of target …
guaranteeing that an optimal policy induced by the function belongs to a set of target …
Policy teaching in reinforcement learning via environment poisoning attacks
We study a security threat to reinforcement learning where an attacker poisons the learning
environment to force the agent into executing a target policy chosen by the attacker. As a …
environment to force the agent into executing a target policy chosen by the attacker. As a …
Informativeness of Reward Functions in Reinforcement Learning
Reward functions are central in specifying the task we want a reinforcement learning agent
to perform. Given a task and desired optimal behavior, we study the problem of designing …
to perform. Given a task and desired optimal behavior, we study the problem of designing …
Causeoccam: Learning interpretable abstract representations in reinforcement learning environments via model sparsity
S Volodin - 2021 - infoscience.epfl.ch
Abstract" I choose this restaurant because they have vegan sandwiches" could be a typical
explanation we would expect from a human. However, current Reinforcement Learning (RL) …
explanation we would expect from a human. However, current Reinforcement Learning (RL) …
[PDF][PDF] Multi-expert Preference Alignment in Reinforcement Learning
L Li - 2024 - repository.tudelft.nl
I was attracted to this project from the beginning, as the concept aligns with what I have
always wanted to pursue since deciding to major in computer science. I am broadly …
always wanted to pursue since deciding to major in computer science. I am broadly …