Publications

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Selected filter tags (click to remove): deep-rl

2022

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 (AAMAS), 2022
Abstract | BibTex | arXiv | Code
AAMASdeep-rlintrinsic-reward

Giuseppe Vecchio, Simone Palazzo, Dario C Guastella, Ignacio Carlucho, Stefano V Albrecht, Giovanni Muscato, Concetto Spampinato
MIDGARD: A Simulation Platform for Autonomous Navigation in Unstructured Environments
ICRA 2022 Workshop on Releasing Robots into the Wild: Simulations, Benchmarks, and Deployment (ICRA), 2022
Abstract | BibTex | arXiv
ICRAdeep-rlsimulator

Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Learning Representations for Control with Hierarchical Forward Models
arXiv:2206.11396, 2022
Abstract | BibTex | arXiv
deep-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 (NeurIPS), 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 (NeurIPS), 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 (NeurIPS), 2021
Abstract | BibTex | arXiv | Code
NeurIPSdeep-rlpolicy-evaluation

Arrasy Rahman, Niklas Höpner, Filippos Christianos, Stefano V. Albrecht
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning
International Conference on Machine Learning (ICML), 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 (ICML), 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 (ICML), 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 (NeurIPS), 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 (AAAI), 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