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Found 7 papers, 2 papers with code
Investigating the Properties of Neural Network Representations in Reinforcement Learning
In this paper we investigate the properties of representations learned by deep reinforcement learning systems.
Continual Auxiliary Task Learning
Learning auxiliary tasks, such as multiple predictions about the world, can provide many benefits to reinforcement learning systems.
Off-Policy Actor-Critic with Emphatic Weightings
A variety of theoretically-sound policy gradient algorithms exist for the on-policy setting due to the policy gradient theorem, which provides a simplified form for the gradient.
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.
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment.
The Utility of Sparse Representations for Control in Reinforcement Learning
We investigate sparse representations for control in reinforcement learning.
Learning Sparse Representations in Reinforcement Learning with Sparse Coding
Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations.
