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02/23/2023
Targeted Search Control in AlphaZero for Effective Policy Improvement
AlphaZero is a self-play reinforcement learning algorithm that achieves ...
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12/20/2022
Settling the Reward Hypothesis
The reward hypothesis posits that, "all of what we mean by goals and pur...
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11/02/2022
Over-communicate no more: Situated RL agents learn concise communication protocols
While it is known that communication facilitates cooperation in multi-ag...
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06/04/2022
Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration
Neural replicator dynamics (NeuRD) is an alternative to the foundational...
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05/24/2022
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games: Corrections
Hindsight rationality is an approach to playing general-sum games that p...
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05/22/2022
Should Models Be Accurate?
Model-based Reinforcement Learning (MBRL) holds promise for data-efficie...
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12/06/2021
Player of Games
Games have a long history of serving as a benchmark for progress in arti...
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11/15/2021
The Partially Observable History Process
We introduce the partially observable history process (POHP) formalism f...
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10/29/2021
Learning to Be Cautious
A key challenge in the field of reinforcement learning is to develop age...
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02/13/2021
Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games
Hindsight rationality is an approach to playing multi-agent, general-sum...
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01/11/2021
Solving Common-Payoff Games with Approximate Policy Iteration
For artificially intelligent learning systems to have widespread applica...
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12/10/2020
Hindsight and Sequential Rationality of Correlated Play
Driven by recent successes in two-player, zero-sum game solving and play...
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11/02/2020
Useful Policy Invariant Shaping from Arbitrary Advice
Reinforcement learning is a powerful learning paradigm in which agents c...
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08/27/2020
The Advantage Regret-Matching Actor-Critic
Regret minimization has played a key role in online learning, equilibriu...
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06/15/2020
Sound Search in Imperfect Information Games
Search has played a fundamental role in computer game research since the...
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06/10/2020
Marginal Utility for Planning in Continuous or Large Discrete Action Spaces
Sample-based planning is a powerful family of algorithms for generating ...
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04/28/2020
Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task
Human-computer interactive systems that rely on machine learning are bec...
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04/20/2020
Approximate exploitability: Learning a best response in large games
A common metric in games of imperfect information is exploitability, i.e...
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12/06/2019
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization
Function approximation is a powerful approach for structuring large deci...
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07/22/2019
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Extensive-form games (EFGs) are a common model of multi-agent interactio...
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06/26/2019
Rethinking Formal Models of Partially Observable Multiagent Decision Making
Multiagent decision-making problems in partially observable environments...
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06/06/2019
Ease-of-Teaching and Language Structure from Emergent Communication
Artificial agents have been shown to learn to communicate when needed to...
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02/01/2019
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for...
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11/04/2018
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
When observing the actions of others, humans carry out inferences about ...
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10/21/2018
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) i...
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09/29/2018
Generalization and Regularization in DQN
Deep reinforcement learning (RL) algorithms have shown an impressive abi...
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09/20/2018
Solving Large Extensive-Form Games with Strategy Constraints
Extensive-form games are a common model for multiagent interactions with...
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09/09/2018
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Learning strategies for imperfect information games from samples of inte...
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07/31/2018
Count-Based Exploration with the Successor Representation
The problem of exploration in reinforcement learning is well-understood ...
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06/05/2018
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces
Dyna is an architecture for reinforcement learning agents that interleav...
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01/06/2017
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Artificial intelligence has seen several breakthroughs in recent years, ...
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12/20/2016
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Evaluating agent performance when outcomes are stochastic and agents use...
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11/28/2014
Solving Games with Functional Regret Estimation
We propose a novel online learning method for minimizing regret in large...
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10/16/2014
Domain-Independent Optimistic Initialization for Reinforcement Learning
In Reinforcement Learning (RL), it is common to use optimistic initializ...
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11/03/2012
Partition Tree Weighting
This paper introduces the Partition Tree Weighting technique, an efficie...
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07/19/2012
The Arcade Learning Environment: An Evaluation Platform for General Agents
In this article we introduce the Arcade Learning Environment (ALE): both...
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06/14/2012
On Local Regret
Online learning aims to perform nearly as well as the best hypothesis in...
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05/03/2012
No-Regret Learning in Extensive-Form Games with Imperfect Recall
Counterfactual Regret Minimization (CFR) is an efficient no-regret learn...
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05/01/2012
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning
We consider the problem of simultaneously learning to linearly combine a...
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12/20/2011
