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surveydeep-rlmulti-agent-rlagent-modellingad-hoc-teamworkautonomous-drivinggoal-recognitionexplainable-aicausalsecurityemergent-communicationiterated-learningintrinsic-rewardsimulatorstate-estimation

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deep-rlStefano-V.-Albrecht

2022

Stefano V. Albrecht, Michael Wooldridge
Special Issue on Multi-Agent Systems Research in the United Kingdom: Guest Editorial
AI Communications, 2022
Abstract | BibTex | Publisher | Special Issue
AICsurveydeep-rlmulti-agent-rlagent-modelling

Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schäfer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht
Deep Reinforcement Learning for Multi-Agent Interaction
AI Communications, 2022
Abstract | BibTex | arXiv | Publisher
AICsurveydeep-rlmulti-agent-rlad-hoc-teamworkagent-modellinggoal-recognitionsecurityexplainable-aiautonomous-driving

Rujie Zhong, Duohan Zhang, Lukas Schäfer, Stefano V. Albrecht, Josiah P. Hanna
Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning
Conference on Neural Information Processing Systems, 2022
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rl

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Representations for Reinforcement Learning with Hierarchical Forward Models
NeurIPS Workshop on Deep Reinforcement Learning, 2022
Abstract | BibTex | arXiv
NeurIPSdeep-rl

Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck, Josiah Hanna, Stefano V. Albrecht
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement Learning
NeurIPS Workshop on Deep Reinforcement Learning, 2022
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlcausal

Guy Azran, Mohamad Hosein Danesh, Stefano V. Albrecht, Sarah Keren
Enhancing Transfer of Reinforcement Learning Agents with Abstract Contextual Embeddings
NeurIPS Workshop on Neuro Causal and Symbolic AI, 2022
Abstract | BibTex
NeurIPSdeep-rlcausal

Lukas Schäfer, Filippos Christianos, Josiah P. Hanna, Stefano V. Albrecht
Decoupled Reinforcement Learning to Stabilise Intrinsically-Motivated Exploration
International Conference on Autonomous Agents and Multi-Agent Systems, 2022
Abstract | BibTex | arXiv | Code
AAMASdeep-rlintrinsic-reward

Arrasy Rahman, Ignacio Carlucho, Niklas Höpner, Stefano V. Albrecht
A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning
arXiv:2210.05448, 2022
Abstract | BibTex | arXiv
ad-hoc-teamworkdeep-rlagent-modelling

Lukas Schäfer, Filippos Christianos, Amos Storkey, Stefano V. Albrecht
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning
arxiv:2207.02249, 2022
Abstract | BibTex | arXiv
deep-rlmulti-agent-rl

Filippos Christianos, Georgios Papoudakis, Stefano V. Albrecht
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
arXiv:2209.14344, 2022
Abstract | BibTex | arXiv
deep-rlmulti-agent-rl

2021

Georgios Papoudakis, Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
Conference on Neural Information Processing Systems, Datasets and Benchmarks Track, 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlmulti-agent-rl

Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Agent Modelling under Partial Observability for Deep Reinforcement Learning
Conference on Neural Information Processing Systems, 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlagent-modelling

Rujie Zhong, Josiah P. Hanna, Lukas Schäfer, Stefano V. Albrecht
Robust On-Policy Data Collection for Data-Efficient Policy Evaluation
NeurIPS Workshop on Offline Reinforcement Learning, 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rl

Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
International Conference on Machine Learning, 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlagent-modellingad-hoc-teamwork

Filippos Christianos, Georgios Papoudakis, Arrasy Rahman, Stefano V. Albrecht
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
International Conference on Machine Learning, 2021
Abstract | BibTex | arXiv | Video | Code
ICMLdeep-rlmulti-agent-rl

Lukas Schäfer, Filippos Christianos, Josiah Hanna, Stefano V. Albrecht
Decoupling Exploration and Exploitation in Reinforcement Learning
ICML Workshop on Unsupervised Reinforcement Learning, 2021
Abstract | BibTex | arXiv | Code
ICMLdeep-rlintrinsic-reward

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
arXiv:2110.04935, 2021
Abstract | BibTex | arXiv | Code
deep-rl

2020

Filippos Christianos, Lukas Schäfer, Stefano V. Albrecht
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
Conference on Neural Information Processing Systems, 2020
Abstract | BibTex | arXiv
NeurIPSdeep-rlmulti-agent-rl

Georgios Papoudakis, Stefano V. Albrecht
Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
AAAI Workshop on Reinforcement Learning in Games, 2020
Abstract | BibTex | arXiv
AAAIdeep-rlagent-modelling

Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Open Ad Hoc Teamwork using Graph-based Policy Learning
arXiv:2006.10412, 2020
Abstract | BibTex | arXiv
deep-rlagent-modellingad-hoc-teamwork

Georgios Papoudakis, Filippos Christianos , Lukas Schäfer, Stefano V. Albrecht
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
arXiv:2006.07869, 2020
Abstract | BibTex | arXiv
deep-rlmulti-agent-rl

Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
Local Information Opponent Modelling Using Variational Autoencoders
arXiv:2006.09447, 2020
Abstract | BibTex | arXiv
deep-rlagent-modelling

2019

Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht
Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning
arXiv:1906.04737, 2019
Abstract | BibTex | arXiv
surveydeep-rlmulti-agent-rl