Reinforcement Learning
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
However, existing unsupervised skill discovery methods often learn entangled skills where one skill variable simultaneously influences many entities in the environment, making downstream skill chaining extremely challenging.
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
Existing solutions either work under very specific assumptions or achieve bounds that are vacuous in some regimes.
Model-FreeActiveExploration inReinforcementLearning
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-specific lower bound ofthe number ofsamples that have to be collected to identify a nearly-optimal policy.
Model-Free Active Exploration in Reinforcement Learning
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. We adopt an information-theoretical viewpoint and start from the instance-specific lower bound of the number of samples that have to be collected to identify a nearly-optimal policy.
