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Found 9 papers, 5 papers with code
Simplified Temporal Consistency Reinforcement Learning
This applies when using pure planning with a dynamics model conditioned on the representation, but, also when utilizing the representation as policy and value function features in model-free RL.
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment.
Learning of feature points without additional supervision improves reinforcement learning from images
In many control problems that include vision, optimal controls can be inferred from the location of the objects in the scene.
Learning to Play Imperfect-Information Games by Imitating an Oracle Planner
We experimentally show that planning with naive Monte Carlo tree search does not perform very well in large combinatorial action spaces.
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement Learning
We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks.
Regularizing Model-Based Planning with Energy-Based Models
Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data.
Regularizing Trajectory Optimization with Denoising Autoencoders
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning.
Semi-Supervised and Active Few-Shot Learning with Prototypical Networks
We consider the problem of semi-supervised few-shot classification where a classifier needs to adapt to new tasks using a few labeled examples and (potentially many) unlabeled examples.
Recurrent Ladder Networks
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models.
