Search Results for author:
Found 16 papers, 7 papers with code
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning
Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy.
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
Student of Games: A unified learning algorithm for both perfect and imperfect information games
Games have a long history as benchmarks for progress in artificial intelligence.
Solving Common-Payoff Games with Approximate Policy Iteration
While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Human-Agent Cooperation in Bridge Bidding
We introduce a human-compatible reinforcement-learning approach to a cooperative game, making use of a third-party hand-coded human-compatible bot to generate initial training data and to perform initial evaluation.
Approximate exploitability: Learning a best response in large games
In prior games research, agent evaluation often focused on the in-practice game outcomes.
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG).
Rethinking Formal Models of Partially Observable Multiagent Decision Making
A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
The new formulation allows estimates to be bootstrapped from other estimates within the same episode, propagating the benefits of baselines along the sampled trajectory; the estimates remain unbiased even when bootstrapping from other estimates.
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available.
Predicting the Performance of IDA* using Conditional Distributions
In this paper we show that, in addition to requiring the heuristic to be consistent, their formulas predictions are accurate only at levels of the brute-force search tree where the heuristic values obey the unconditional distribution that they defined and then used in their formula.
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions
In this paper, we present a new MCCFR algorithm, Average Strategy Sampling (AS), that samples a subset of the player's actions according to the player's average strategy.
Bayes' Bluff: Opponent Modelling in Poker
We demonstrate methods for playing effective responses to the opponent, based on the posterior.
