Tabish Rashid

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Occurrence Frequency

Reinforcement Learning

Top Venues

Publications

Adam Jelley, Yuhan Cao, David Bignell, Sam Devlin, Tabish Rashid
Aligning Agents like Large Language Models. CoRR (2024)
Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
Imitating Human Behaviour with Diffusion Models. ICLR (2023)
Lukas Schäfer, Logan Jones, Anssi Kanervisto, Yuhan Cao, Tabish Rashid, Raluca Georgescu, David Bignell, Siddhartha Sen, Andrea Treviño Gavito, Sam Devlin
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games. CoRR (2023)
Tim Pearce, Tabish Rashid, Anssi Kanervisto, David Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, Sam Devlin
Imitating Human Behaviour with Diffusion Models. CoRR (2023)
Tabish Rashid, Cheng Zhang, Kamil Ciosek
Estimating α-Rank by Maximizing Information Gain. CoRR (2021)
Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson
Regularized Softmax Deep Multi-Agent Q-Learning. NeurIPS (2021)
Bei Peng, Tabish Rashid, Christian Schröder de Witt, Pierre-Alexandre Kamienny, Philip H. S. Torr, Wendelin Boehmer, Shimon Whiteson
FACMAC: Factored Multi-Agent Centralised Policy Gradients. NeurIPS (2021)
Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson
Softmax with Regularization: Better Value Estimation in Multi-Agent Reinforcement Learning. CoRR (2021)
Tabish Rashid, Cheng Zhang, Kamil Ciosek
Estimating α-Rank by Maximizing Information Gain. AAAI (2021)
Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. NeurIPS (2020)
Tabish Rashid, Bei Peng, Wendelin Böhmer, Shimon Whiteson
Optimistic Exploration even with a Pessimistic Initialisation. CoRR (2020)
Tabish Rashid, Bei Peng, Wendelin Boehmer, Shimon Whiteson
Optimistic Exploration even with a Pessimistic Initialisation. ICLR (2020)
Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson
Weighted QMIX: Expanding Monotonic Value Function Factorisation. CoRR (2020)
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. CoRR (2020)
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. J. Mach. Learn. Res. 21 (2020)
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
MAVEN: Multi-Agent Variational Exploration. NeurIPS (2019)
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson
The StarCraft Multi-Agent Challenge. AAMAS (2019)
Wendelin Böhmer, Tabish Rashid, Shimon Whiteson
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning. CoRR (2019)
Mikayel Samvelyan, Tabish Rashid, Christian Schröder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob N. Foerster, Shimon Whiteson
The StarCraft Multi-Agent Challenge. CoRR (2019)
Anuj Mahajan, Tabish Rashid, Mikayel Samvelyan, Shimon Whiteson
MAVEN: Multi-Agent Variational Exploration. CoRR (2019)
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. CoRR (2018)
Tabish Rashid, Mikayel Samvelyan, Christian Schröder de Witt, Gregory Farquhar, Jakob N. Foerster, Shimon Whiteson
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. ICML (2018)
Tabish Rashid, Ioannis Agrafiotis, Jason R. C. Nurse
A New Take on Detecting Insider Threats: Exploring the Use of Hidden Markov Models. MIST@CCS (2016)