Search Results for author:
Found 6 papers, 0 papers with code
Five Properties of Specific Curiosity You Didn't Know Curious Machines Should Have
As a second main contribution of this work, we show how these properties may be implemented together in a proof-of-concept reinforcement learning agent: we demonstrate how the properties manifest in the behaviour of this agent in a simple non-episodic grid-world environment that includes curiosity-inducing locations and induced targets of curiosity.
Prototyping three key properties of specific curiosity in computational reinforcement learning
Curiosity for machine agents has been a focus of intense research.
Efficient Planning in Large MDPs with Weak Linear Function Approximation
Large-scale Markov decision processes (MDPs) require planning algorithms with runtime independent of the number of states of the MDP.
Discounted Reinforcement Learning Is Not an Optimization Problem
Discounted reinforcement learning is fundamentally incompatible with function approximation for control in continuing tasks.
Differentially Private Contextual Linear Bandits
We study the contextual linear bandit problem, a version of the standard stochastic multi-armed bandit (MAB) problem where a learner sequentially selects actions to maximize a reward which depends also on a user provided per-round context.
Conservative Bandits
We consider both the stochastic and the adversarial settings, where we propose, natural, yet novel strategies and analyze the price for maintaining the constraints.
