DCRAC: DEEP CONDITIONED RECURRENT ACTOR-CRITIC FOR MULTI-OBJECTIVE PARTIALLY OBSERVABLE ENVIRONMENTS

von Nian, Xiaodong
Veröffentlicht in AAMAS (19. : 2020 : Online) International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020) ; Volume 2 of 3 (2021)

COOPERATIVE PRIORITIZED SWEEPING

von Bargiacchi, Eugenio
Veröffentlicht in AAMAS (20. : 2021 : Online) 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) ; Volume 1 of 3 (2021)

Deep Reinforcement Learning for Solving Train Unit Shunting Problem with Interval Timing

von Lee, Wan-Jui
Veröffentlicht in EDCC (16. : 2020 : München) Dependable computing - EDCC 2020 Workshops (2020)

Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs

von Roijers, Diederik M.
Veröffentlicht in International Conference on Automated Planning and Scheduling (28. : 2018 : Delft) Proceedings of the Twenty-eighth International Conference on Automated Planning and Scheduling (2018)

MULTI-OBJECTIVE MULTI-AGENT DECISION MAKING: A UTILITY-BASED ANALYSIS AND SURVEY

von Rădulescu, Roxana
Veröffentlicht in AAMAS (19. : 2020 : Online) International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020) ; Volume 3 of 3 (2021)

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

von Steckelmacher, Denis
Veröffentlicht in ECML PKDD (4. : 2019 : Würzburg) Machine learning and knowledge discovery in databases ; Part 3 (2020)

OPPONENT MODELLING FOR REINFORCEMENT LEARNING IN MULTI-OBJECTIVE NORMAL FORM GAMES

von Zhang, Yijie
Veröffentlicht in AAMAS (19. : 2020 : Online) International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020) ; Volume 3 of 3 (2021)

DISTRIBUTIONAL MONTE CARLO TREE SEARCH FOR RISK-AWARE AND MULTI- OBJECTIVE REINFORCEMENT LEARNING

von Hayes, Conor F.
Veröffentlicht in AAMAS (20. : 2021 : Online) 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) ; Volume 3 of 3 (2021)

THOMPSON SAMPLING FOR FACTORED MULTI-AGENT BANDITS

von Verstraeten, Timothy
Veröffentlicht in AAMAS (19. : 2020 : Online) International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020) ; Volume 3 of 3 (2021)

Interactive Multi-objective Reinforcement Learning in Multi-armed Bandits with Gaussian Process Utility Models

von Roijers, Diederik M.
Veröffentlicht in ECML/PKDD (2020 : Online) Machine learning and knowledge discovery in databases ; Part 3 (2021)

SCALABLE OPTIMIZATION FOR WIND FARM CONTROL USING COORDINATION GRAPHS

von Verstraeten, Timothy
Veröffentlicht in AAMAS (20. : 2021 : Online) 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) ; Volume 3 of 3 (2021)