Dazeley, Richard

A practical guide to multi-objective reinforcement learning and planning

Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., et al. (2022) · Autonomous Agents and Multi-Agent Systems

Reinforcement Learning in RoboticsAdvanced Multi-Objective Optimization Algorithms

Cited by 400

A multi-objective deep reinforcement learning framework

Nguyen, T. T., Nguyen, N. D., Vamplew, P., Nahavandi, S., Dazeley, R., & Lim, C. P. (2020) · Engineering Applications of Artificial Intelligence

Reinforcement Learning in RoboticsAdvanced Multi-Objective Optimization Algorithms

Cited by 152

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021)

Vamplew, P., Smith, B. J., Källström, J., Ramos, G., Rădulescu, R., Roijers, D. M., Hayes, C. F., Heintz, F., Mannion, P., Libin, P. J. K., et al. (2022) · Autonomous Agents and Multi-Agent Systems

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 41

AI apology: a critical review of apology in AI systems

Harland, H., Dazeley, R., Senaratne, H., Vamplew, P., Cruz, F., & Nakisa, B. (2025) · Artificial Intelligence Review

Social Robot Interaction and HRIAI in Service Interactions

Cited by 19

A NetHack Learning Environment Language Wrapper for Autonomous Agents

Goodger, N., Vamplew, P., Foale, C., & Dazeley, R. (2023) · Journal of Open Research Software

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 1