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Found 7 papers, 2 papers with code
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations.
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning
Learning models of the environment from pure interaction is often considered an essential component of building lifelong reinforcement learning agents.
Model-based Reinforcement Learning
reinforcement-learning
+1
Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning
After viewing them through the lens of dynamic programming, we first consider the classical instantiations of these planning styles and provide theoretical results and hypotheses on which one will perform better in the pure planning, planning & learning, and transfer learning settings.
Model-based Reinforcement Learning
reinforcement-learning
+2
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates
We study the problem of learning a good set of policies, so that when combined together, they can solve a wide variety of unseen reinforcement learning tasks with no or very little new data.
What is Going on Inside Recurrent Meta Reinforcement Learning Agents?
Recurrent meta reinforcement learning (meta-RL) agents are agents that employ a recurrent neural network (RNN) for the purpose of "learning a learning algorithm".
A Brief Look at Generalization in Visual Meta-Reinforcement Learning
Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training environments, there have been several studies that investigated the generalization performance of these algorithms.
Attentive Deep Regression Networks for Real-Time Visual Face Tracking in Video Surveillance
Visual face tracking is one of the most important tasks in video surveillance systems.
