Search Results for author:
Found 10 papers, 3 papers with code
Fast computation of Nash Equilibria in Imperfect Information Games
We introduce and analyze a class of algorithms, called Mirror Ascent against an Improved Opponent (MAIO), for computing Nash equilibria in two-player zero-sum games, both in normal form and in sequential imperfect information form.
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).
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Reward-Respecting Subtasks for Model-Based Reinforcement Learning
Each subtask is solved to produce an option, and then a model of the option is learned and made available to the planning process.
Model-based Reinforcement Learning
reinforcement-learning
+1
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
The Advantage Regret-Matching Actor-Critic
In this paper, we describe a general model-free RL method for no-regret learning based on repeated reconsideration of past behavior.
Approximate exploitability: Learning a best response in large games
In prior games research, agent evaluation often focused on the in-practice game outcomes.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents.
