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Found 9 papers, 1 papers with code
Value function interference and greedy action selection in value-based multi-objective reinforcement learning
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives, represented by vector-valued rewards.
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning
reinforcement-learning
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function.
Levels of explainable artificial intelligence for human-aligned conversational explanations
Over the last few years there has been rapid research growth into eXplainable Artificial Intelligence (XAI) and the closely aligned Interpretable Machine Learning (IML).
Persistent Rule-based Interactive Reinforcement Learning
Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-time.
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
When interacting with a learner agent, humans may provide either evaluative or informative advice.
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
In this work, while reviewing externally-influenced methods, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering collaboration by classifying and comparing various methods that use external information in the learning process.
Discrete-to-Deep Supervised Policy Learning
It then selects from each discrete state an input value and the action with the highest numerical preference as an input/target pair.
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 approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions.
