我们的愿景 Our vision

Papers

Rationality and intelligence

AI safety gridworlds

Modeling Friends and Foes

Forget-me-not-Process

Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study

Universal Transformers

Graph Convolutional Policy Network

Thermodynamics as a theory of decision-making with informationprocessing costs

Concrete Problems in AI Safety

A course in game theory

Theory of games and economic behavior

Reinforcement learning: An introduction 1e

Regret analysis of stochastic and nonstochastic multi-armed bandit problems

The nonstochastic multiarmed bandit problem

Information theory of decisions and actions

Clustering with bregman divergences

Quantal Response Equilibria for Normal Form Games

The numerics of gans

The Mechanics of n-Player Differentiable Games

Reactive bandits with attitude

Data clustering by markovian relaxation and the information bottleneck method

Information bottleneck for Gaussian variables

Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimal

Risk sensitive path integral control

Information, utility and bounded rationality

Hysteresis effects of changing the parameters of noncooperative games

The best of both worlds: stochastic and adversarial bandits

One practical algorithm for both stochastic and adversarial bandits

An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits

Friend-or-Foe Q-Learning in General-Sum Games

New criteria and a new algorithm for learning in multi-agent systems

Correlated Q-Learning

Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning

Learning against sequential opponents in repeated stochastic games

On the likelihood that one unknown probability exceeds another in view of the evidence of two sample

An empirical evaluation of Thompson Sampling

What game are we playing? end-to-end learning in normal and extensive form games

Intriguing properties of neural networks

Explaining and harnessing adversarial examples

go-explore

The Landscape of Deep Reinforcement Learning

用因果影响图建模通用人工智能安全框架