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A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-...
03/17/2021 ∙ by Conor F. Hayes, et al. ∙
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Persistent Rule-based Interactive Reinforcement Learning
Interactive reinforcement learning has allowed speeding up the learning ...
02/04/2021 ∙ by Adam Bignold, et al. ∙
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Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
Reinforcement learning is an approach used by intelligent agents to auto...
09/21/2020 ∙ by Adam Bignold, et al. ∙
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A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
A long-term goal of reinforcement learning agents is to be able to perfo...
07/03/2020 ∙ by Adam Bignold, et al. ∙
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Explainable robotic systems: Interpreting outcome-focused actions in a reinforcement learning scenario
Robotic systems are more present in our society every day. In human-robo...
06/24/2020 ∙ by Francisco Cruz, et al. ∙
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Discrete-to-Deep Supervised Policy Learning
Neural networks are effective function approximators, but hard to train ...
05/05/2020 ∙ by Budi Kurniawan, et al. ∙
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A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions
We report a previously unidentified issue with model-free, value-based a...
04/14/2020 ∙ by Peter Vamplew, et al. ∙
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A Survey of Multi-Objective Sequential Decision-Making
Sequential decision-making problems with multiple objectives arise natur...
02/04/2014 ∙ by Diederik Marijn Roijers, et al. ∙
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