2 citations · 4 across the 7 of their papers we have counts for
10 papers · 1 filter
Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning
Parham Mohammad Panahi, Armin Ashrafi, Haoyu Du +3
Experience replay remains one of the most practical and useful algorithmic tools in the deep reinforcement learning (DRL) toolbox. Aside from the limited success of prioritized rep…
Fine-Tuning without Performance Degradation
Han Wang, Adam White, Martha White
Fine-tuning policies learned offline remains a major challenge in application domains. Monotonic performance improvement during \emph{fine-tuning} is often challenging, as agents t…
A Method for Evaluating Hyperparameter Sensitivity in Reinforcement Learning
Jacob Adkins, Michael Bowling, Adam White
The performance of modern reinforcement learning algorithms critically relies on tuning ever-increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lea…
Real-Time Recurrent Learning using Trace Units in Reinforcement Learning
Esraa Elelimy, Adam White, Michael Bowling +1
Recurrent Neural Networks (RNNs) are used to learn representations in partially observable environments. For agents that learn online and continually interact with the environment,…
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy +2
This paper introduces a new empirical methodology, the Cross-environment Hyperparameter Setting Benchmark, that compares RL algorithms across environments using a single hyperparam…
Investigating the Interplay of Prioritized Replay and Generalization
Parham Mohammad Panahi, Andrew Patterson, Martha White +1
Experience replay, the reuse of past data to improve sample efficiency, is ubiquitous in reinforcement learning. Though a variety of smart sampling schemes have been introduced to…