Due to the dynamic differences of different samples, our CCF employs specific fusion constraints for each individual in practice.

How the US left Ukraine exposed to Russia's winter war Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? Russia-Ukraine talks resume in Geneva as US claims'meaningful' progress Day two of the third round of trilateral talks between Russia, Ukraine and the United States is under way in Geneva, Switzerland, as the four-year anniversary of Russia's full-scale invasion of its neighbour looms next week, with vague references to "progress" but nothing tangible yet shared. Little has been made public about the talks' contents since negotiations kicked off on Tuesday behind closed doors and continued on Wednesday morning. The thorniest of issues, territory and the yielding of it, remains the key sticking point.

Hybrid Reinforcement Learning (RL), where an agent learns from both an offline dataset and online explorations in an unknown environment, has garnered significant recent interest. A crucial question posed by Xie et al. (2022b) is whether hybrid RL can improve upon the existing lower bounds established for purely of-fline or online RL without requiring that the behavior policy visit every state and action the optimal policy does. While Li et al. (2023b) provided an affirmative answer for tabular P AC RL, the question remains unsettled for both the regret-minimizing and non-tabular cases. In this work, building upon recent advancements in offline RL and reward-agnostic exploration, we develop computationally efficient algorithms for both P AC and regret-minimizing RL with linear function approximation, without requiring concentrability on the entire state-action space. We demonstrate that these algorithms achieve sharper error or regret bounds that are no worse than, and can improve on, the optimal sample complexity in offline RL (the first algorithm, for P AC RL) and online RL (the second algorithm, for regret-minimizing RL) in linear Markov decision processes (MDPs), regardless of the quality of the behavior policy. To our knowledge, this work establishes the tightest theoretical guarantees currently available for hybrid RL in linear MDPs.

Experimental results on six widely-used benchmarks demonstrate that compared to baselines,DEEPEN achieves consistent improvements across all benchmarks.

Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence.