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Found 6 papers, 1 papers with code
Auxiliary task discovery through generate-and-test
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning.
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Our focus on the role of the target policy of the auxiliary tasks is motivated by the fact that the target policy determines the behavior about which the agent wants to make a prediction and the state-action distribution that the agent is trained on, which further affects the main task learning.
From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning
We present three new diagnostic prediction problems inspired by classical-conditioning experiments to facilitate research in online prediction learning.
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Unfortunately, the performance of deep reinforcement learning systems is sensitive to hyper-parameter settings and architecture choices.
Two geometric input transformation methods for fast online reinforcement learning with neural nets
We apply neural nets with ReLU gates in online reinforcement learning.
A First Empirical Study of Emphatic Temporal Difference Learning
In this paper we present the first empirical study of the emphatic temporal-difference learning algorithm (ETD), comparing it with conventional temporal-difference learning, in particular, with linear TD(0), on on-policy and off-policy variations of the Mountain Car problem.
