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Found 8 papers, 2 papers with code
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks.
Settling the Reward Hypothesis
The reward hypothesis posits that, "all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scalar signal (reward)."
Should Models Be Accurate?
We focus our investigations on Dyna-style planning in a prediction setting.
Adapting the Function Approximation Architecture in Online Reinforcement Learning
However, prevailing optimization techniques are not designed for strictly-incremental online updates.
Variational Filtering with Copula Models for SLAM
The ability to infer map variables and estimate pose is crucial to the operation of autonomous mobile robots.
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain.
On Catastrophic Interference in Atari 2600 Games
Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.
Stochastically Dominant Distributional Reinforcement Learning
We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm.
Distributional Reinforcement Learning
reinforcement-learning
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