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Found 6 papers, 1 papers with code
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
On the Geometry of Reinforcement Learning in Continuous State and Action Spaces
Central to our work is the idea that the transition dynamics induce a low dimensional manifold of reachable states embedded in the high-dimensional nominal state space.
Effects of Data Geometry in Early Deep Learning
Deep neural networks can approximate functions on different types of data, from images to graphs, with varied underlying structure.
Meta-Learning Parameterized Skills
We propose a novel parameterized skill-learning algorithm that aims to learn transferable parameterized skills and synthesize them into a new action space that supports efficient learning in long-horizon tasks.
Hyperbolic Embeddings for Learning Options in Hierarchical Reinforcement Learning
Hierarchical reinforcement learning deals with the problem of breaking down large tasks into meaningful sub-tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Natural Option Critic
In this paper we show how the option-critic architecture can be extended to estimate the natural gradient of the expected discounted return.
