引用次数
引用: 2,532
H-指数: 24
I10-指数 : 32
出版物: 106
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learning
Sabrina McCallum , Max Taylor-Davies , Stefano V Albrecht , Alessandro Suglia
arXiv preprint arXiv:2312.04736
2023
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning
Samuel Garcin , James Doran , Shangmin Guo , Christopher G Lucas
arXiv preprint arXiv:2310.03494
2023
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning
Trevor McInroe , Lukas Schäfer , Stefano V Albrecht
Transactions on Machine Learning Research
2023
Causal Social Explanations for Stochastic Sequential Multi-Agent Decision-Making.
Balint Gyevnar , Cheng Wang , Christopher G Lucas , Shay B Cohen
CoRR
2023
Learning Representations for Reinforcement Learning with Hierarchical Forward Models
Trevor McInroe , Lukas Schäfer , Stefano V Albrecht
2022
Interpretable Goal Recognition in the Presence of Hidden Obstacles for Autonomous Vehicles
Josiah P Hanna , Arrasy Rahman , Elliot Fosong , Francisco Eiras
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
2021
An Optimization-based Motion Planner for Safe Autonomous Driving
Francisco Eiras , Majd Hawasly , Stefano V Albrecht , Ram Ramamoorthy
Second (virtual) workshop on Robust autonomy: Safe robot learning and control in uncertain real-world environments
2020
Reasoning about Unforeseen Possibilities During Policy Learning
Craig Innes , Alex Lascarides , Stefano V Albrecht , Subramanian Ramamoorthy
arXiv preprint arXiv:1801.03331
2018
Exploiting causality for efficient monitoring in POMDPs
Stefano V Albrecht , Subramanian Ramamoorthy
arXiv preprint arXiv:1401.7941
2014
Automated Agents in Financial Markets and the Problem of Ad Hoc Coordination in Heterogeneous Competitive Agent Populations
Stefano V Albrecht , Subramanian Ramamoorthy
Models and Algorithms for Ad Hoc Coordination in Multiagent Systems
Stefano V Albrecht
Comparison of Multiagent Learning Algorithms in Ad Hoc Teams
Stefano V Albrecht
A game-theoretic model and best-response learning method for ad hoc coordination in multiagent systems
Stefano V. Albrecht , Subramanian Ramamoorthy
adaptive agents and multi-agents systems 1155 -1156
56
2013
Ad hoc coordination in multiagent systems with applications to human-machine interaction
Stefano V. Albrecht , Subramanian Ramamoorthy
adaptive agents and multi-agents systems 1415 -1416
1
2013
Exploiting causality for selective belief filtering in dynamic bayesian networks
Stefano V. Albrecht , Subramanian Ramamoorthy
Journal of Artificial Intelligence Research 55 ( 1) 1135 -1178
6
2016
Ad Hoc Coordination in Multiagent Systems with Applications to Human-Machine Interaction (Doctoral Consortium)
Stefano V. Albrecht
2013
On convergence and optimality of best-response learning with policy types in multiagent systems
Stefano V. Albrecht , Subramanian Ramamoorthy
uncertainty in artificial intelligence 12 -21
21
2014
An empirical study on the practical impact of prior beliefs over policy types
Stefano V. Albrecht , Jacob W. Crandall , Subramanian Ramamoorthy
national conference on artificial intelligence 1988 -1994
11
2015
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning.
Stefano V. Albrecht , Georgios Papoudakis , Arrasy Rahman , Filippos Christianos
arXiv: Learning
126
2019
Reasoning about Hypothetical Agent Behaviours and their Parameters
Stefano V. Albrecht , Peter Stone
adaptive agents and multi agents systems 547 -555
22
2017
Filippos Christianos Lukas Schäfer Georgios Papoudakis Muhammad Arrasy Rahman Peter Stone Francisco Eiras Ignacio Carlucho Balint Gyevnar Mihai Dobre Josiah P. Hanna Cillian Brewitt Majd Hawasly Cheng Wang Jacob W. Crandall Morris Antonello Trevor McInroe Samuel Garcin John Wilhelm Christopher G. Lucas Mohan Sridharan