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Found 6 papers, 2 papers with code
Lipschitz Lifelong Reinforcement Learning
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
time; 2) we consider a planning agent using the current model of the environment but unaware of its future evolution.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Open Loop Execution of Tree-Search Algorithms, extended version
In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action.
Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring
Autonomous unpowered flight is a challenge for control and guidance systems: all the energy the aircraft might use during flight has to be harvested directly from the atmosphere.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning, Extended version
time; 2) we consider a planning agent using the current model of the environment but unaware of its future evolution.
DARTS-PRIME: Regularization and Scheduling Improve Constrained Optimization in Differentiable NAS
Differentiable Architecture Search (DARTS) is a recent neural architecture search (NAS) method based on a differentiable relaxation.
Ranked #10 on
Neural Architecture Search
on CIFAR-100
