From the 1 of 9 linked papers with an AI index.
9 papers
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
Emil Mittag, Richard Dazeley, Peter Vamplew
The paper introduces OOD-RL-Bench, a benchmark framework for evaluating out-of-distribution detection methods in reinforcement learning by injecting various anomalies into RL traje…
Memory Merge DQN: Sensitivity Weighted Target Updates for Stable Value Learning
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-networks use target networks to stabilise bootstrapped value learning, but the standard hard copy update also introduces a tradeoff. Holding the target network fixed, improv…
Ensemble Elastic DQN: A Step Dependent Ensemble Approach for Reducing Overestimation in Deep Value-Based Reinforcement Learning
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-Networks (DQN) can suffer from overestimation bias because bootstrapped targets use a maximisation operation over noisy value estimates. Ensemble-based methods and multi-ste…
Issues with Value-Based Multi-objective Reinforcement Learning: Value Function Interference and Overestimation Sensitivity
Peter Vamplew, Ethan, Watkins +2
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives,…
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
Peter Vamplew, Cameron Foale
This research note identifies a previously overlooked distinction between multi-objective reinforcement learning (MORL), and more conventional single-objective reinforcement learni…
Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning
Andrés Holgado-Sánchez, Peter Vamplew, Richard Dazeley +2
Value-aware AI should recognise human values and adapt to the value systems (value-based preferences) of different users. This requires operationalization of values, which can be p…