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Found 5 papers, 1 papers with code
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
We propose a novel Bayesian approach to solve stochastic optimization problems that involve finding extrema of noisy, nonlinear functions.
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).
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy.
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
