no code implementations • 6 Feb 2024 • Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Marc Rigter, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang
The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.
no code implementations • 25 Sep 2023 • Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson
However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.
1 code implementation • 16 Feb 2023 • Shangeth Rajaa, Kriti Anandan, Swaraj Dalmia, Tarun Gupta, Eng Siong Chng
The pre-trained multi-lingual XLSR model generalizes well for language identification after fine-tuning on unseen languages.
no code implementations • 9 Nov 2022 • Tarun Gupta, Peter Karkus, Tong Che, Danfei Xu, Marco Pavone
Effectively exploring the environment is a key challenge in reinforcement learning (RL).
1 code implementation • 22 Mar 2022 • Tarun Gupta, Duc-Tuan Truong, Tran The Anh, Chng Eng Siong
In this work, we propose a bi-encoder transformer mixture model for speaker age and height estimation.
no code implementations • 31 Jan 2022 • Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis, Mingfei Sun, Tim Rocktäschel, Shimon Whiteson
Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS.
no code implementations • 27 Apr 2021 • Bozhidar Vasilev, Tarun Gupta, Bei Peng, Shimon Whiteson
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios.
no code implementations • 26 Feb 2021 • Abhisek Ghosal, Tarun Gupta, Kishalay Mahato, Amlan K. Roy
Photon-induced electronic excitations are ubiquitously observed in organic chromophore.
Chemical Physics
6 code implementations • 18 Nov 2020 • Christian Schroeder de Witt, Tarun Gupta, Denys Makoviichuk, Viktor Makoviychuk, Philip H. S. Torr, Mingfei Sun, Shimon Whiteson
Most recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
no code implementations • 6 Oct 2020 • Tarun Gupta, Anuj Mahajan, Bei Peng, Wendelin Böhmer, Shimon Whiteson
VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities.
Multi-agent Reinforcement Learning reinforcement-learning +3
2 code implementations • ICLR 2021 • Tonghan Wang, Tarun Gupta, Anuj Mahajan, Bei Peng, Shimon Whiteson, Chongjie Zhang
Learning a role selector based on action effects makes role discovery much easier because it forms a bi-level learning hierarchy -- the role selector searches in a smaller role space and at a lower temporal resolution, while role policies learn in significantly reduced primitive action-observation spaces.
no code implementations • 30 Apr 2019 • Nagdev Amruthnath, Tarun Gupta
Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes.