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Found 7 papers, 5 papers with code
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks
In this effort, we consider the impact of regularization on the diversity of actions taken by policies generated from reinforcement learning agents trained using a policy gradient.
On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks
In this effort we consider a reinforcement learning (RL) technique for solving personalization tasks with complex reward signals.
Offline Policy Comparison under Limited Historical Agent-Environment Interactions
We address the challenge of policy evaluation in real-world applications of reinforcement learning systems where the available historical data is limited due to ethical, practical, or security considerations.
Analysis of Deep Neural Networks with Quasi-optimal polynomial approximation rates
The construction of the proposed neural network is based on a quasi-optimal polynomial approximation.
Neural network integral representations with the ReLU activation function
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function.
Robust learning with implicit residual networks
In this effort, we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes.
Greedy Shallow Networks: An Approach for Constructing and Training Neural Networks
We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function.
