Search Results for author:
Found 6 papers, 2 papers with code
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
This paper addresses the challenge of optimizing meta-parameters (i. e., hyperparameters) in machine learning algorithms, a critical factor influencing training efficiency and model performance.
Step-size Optimization for Continual Learning
We conclude by suggesting that combining both approaches could be a promising future direction to improve the performance of neural networks in continual learning.
Toward Efficient Gradient-Based Value Estimation
Gradient-based methods for value estimation in reinforcement learning have favorable stability properties, but they are typically much slower than Temporal Difference (TD) learning methods.
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.
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.
Behaviour Suite for Reinforcement Learning
bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.
