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Found 5 papers, 4 papers with code
Learning Group Structure and Disentangled Representations of Dynamical Environments
Learning disentangled representations is a key step towards effectively discovering and modelling the underlying structure of environments.
Robust Visual Domain Randomization for Reinforcement Learning
Producing agents that can generalize to a wide range of visually different environments is a significant challenge in reinforcement learning.
Robust Domain Randomization for Reinforcement Learning
In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.
Exploratory Combinatorial Optimization with Reinforcement Learning
Our approach of exploratory combinatorial optimization (ECO-DQN) is, in principle, applicable to any combinatorial problem that can be defined on a graph.
Estimating Risk and Uncertainty in Deep Reinforcement Learning
Reinforcement learning agents are faced with two types of uncertainty.
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