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Found 7 papers, 2 papers with code
Unsupervised Discovery of Continuous Skills on a Sphere
However, most of the existing methods learn a finite number of discrete skills, and thus the variety of behaviors that can be exhibited with the learned skills is limited.
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
To make REDQ more computationally efficient, we propose a method of improving computational efficiency called DroQ, which is a variant of REDQ that uses a small ensemble of dropout Q-functions.
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
It learns a belief model over the embedding space and a belief-conditional policy and Q-function.
Meta-Model-Based Meta-Policy Optimization
Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings.
Optimistic Proximal Policy Optimization
Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains.
Learning Robust Options by Conditional Value at Risk Optimization
While there are several methods to learn options that are robust against the uncertainty of model parameters, these methods only consider either the worst case or the average (ordinary) case for learning options.
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
In this paper, we propose a framework that can automatically refine symbol grounding functions and a high-level planner to reduce human effort for designing these modules.
