812 citations · 1.4k across the 24 of their papers we have counts for
7 papers · 1 filter
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
Jakob N. Foerster, Francis Song, Edward Hughes +5
When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions wi…
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi +4
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algo…
Generalization and Regularization in DQN
Jesse Farebrother, Marlos C. Machado, Michael Bowling
Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performanc…
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Martin Schmid, Neil Burch, Marc Lanctot +3
Learning strategies for imperfect information games from samples of interaction is a challenging problem. A common method for this setting, Monte Carlo Counterfactual Regret Minimi…
Solving Large Extensive-Form Games with Strategy Constraints
Trevor Davis, Kevin Waugh, Michael Bowling
Extensive-form games are a common model for multiagent interactions with imperfect information. In two-player zero-sum games, the typical solution concept is a Nash equilibrium ove…
Count-Based Exploration with the Successor Representation
Marlos C. Machado, Marc G. Bellemare, Michael Bowling
In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but tha…