1.1k citations · 5k across the 117 of their papers we have counts for
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Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency
Mingfei Sun, Sam Devlin, Katja Hofmann +1
Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation t…
On the Practical Consistency of Meta-Reinforcement Learning Algorithms
Zheng Xiong, Luisa Zintgraf, Jacob Beck +2
Consistency is the theoretical property of a meta learning algorithm that ensures that, under certain assumptions, it can adapt to any task at test time. An open question is whethe…
Reinforcement Learning in Factored Action Spaces using Tensor Decompositions
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factor…
Model based Multi-agent Reinforcement Learning with Tensor Decompositions
Pascal Van Der Vaart, Anuj Mahajan, Shimon Whiteson
A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces. Inspired from Tesseract [Mahajan et al., 2021], this position pa…
Truncated Emphatic Temporal Difference Methods for Prediction and Control
Shangtong Zhang, Shimon Whiteson
Emphatic Temporal Difference (TD) methods are a class of off-policy Reinforcement Learning (RL) methods involving the use of followon traces. Despite the theoretical success of emp…
Communicating via Markov Decision Processes
Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl +6
We consider the problem of communicating exogenous information by means of Markov decision process trajectories. This setting, which we call a Markov coding game (MCG), generalizes…
Bayesian Bellman Operators
Matthew Fellows, Kristian Hartikainen, Shimon Whiteson
We introduce a novel perspective on Bayesian reinforcement learning (RL); whereas existing approaches infer a posterior over the transition distribution or Q-function, we character…
SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching
Mingfei Sun, Anuj Mahajan, Katja Hofmann +1
We present SoftDICE, which achieves state-of-the-art performance for imitation learning. SoftDICE fixes several key problems in ValueDICE, an off-policy distribution matching appro…
Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning
Anuj Mahajan, Mikayel Samvelyan, Lei Mao +6
Reinforcement Learning in large action spaces is a challenging problem. Cooperative multi-agent reinforcement learning (MARL) exacerbates matters by imposing various constraints on…
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
Bozhidar Vasilev, Tarun Gupta, Bei Peng +1
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenari…
Regularized Softmax Deep Multi-Agent -Learning
Ling Pan, Tabish Rashid, Bei Peng +2
Tackling overestimation in -learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little atte…
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Charlie Blake, Vitaly Kurin, Maximilian Igl +1
Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior…
Breaking the Deadly Triad with a Target Network
Shangtong Zhang, Hengshuai Yao, Shimon Whiteson
The deadly triad refers to the instability of a reinforcement learning algorithm when it employs off-policy learning, function approximation, and bootstrapping simultaneously. In t…
Deep Interactive Bayesian Reinforcement Learning via Meta-Learning
Luisa Zintgraf, Sam Devlin, Kamil Ciosek +2
Agents that interact with other agents often do not know a priori what the other agents' strategies are, but have to maximise their own online return while interacting with and lea…
Average-Reward Off-Policy Policy Evaluation with Function Approximation
Shangtong Zhang, Yi Wan, Richard S. Sutton +1
We consider off-policy policy evaluation with function approximation (FA) in average-reward MDPs, where the goal is to estimate both the reward rate and the differential value func…