Search Results for author:
Found 14 papers, 7 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).
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.
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.
Ranked #1 on
Atari Games
on Atari 2600 Alien
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.
Consistent Jumpy Predictions for Videos and Scenes
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Deep reinforcement learning with relational inductive biases
We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability.
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.
Consistent Generative Query Networks
These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive.
Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.
Efficient Neural Audio Synthesis
The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system.
