Search Results for author:
Found 2 papers, 0 papers with code
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains
The second level has the teacher probabilities of sampled labels available in addition, which turns out to boost the convergence rate lower bound to ${{|{\mathcal S}||{\mathcal A}|}/{n}}$.
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by $1$, and proved regret bounds that have a polylogarithmic dependence on the planning horizon $H$.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
