We study reinforcement learning (RL) with linear function approximation....
We consider learning Nash equilibria in two-player zero-sum Markov Games...
Recent studies have shown that episodic reinforcement learning (RL) is n...
We consider learning a stochastic bandit model, where the reward functio...
Thanks to the power of representation learning, neural contextual bandit...
Escaping from saddle points and finding local minima is a central proble...
We study the linear contextual bandit problem in the presence of adversa...
We study the model-based reward-free reinforcement learning with linear
...
We study pure exploration in bandits, where the dimension of the feature...
We study the off-policy evaluation (OPE) problem in reinforcement learni...
The success of deep reinforcement learning (DRL) is due to the power of
...
We study reinforcement learning (RL) with linear function approximation....
In many sequential decision-making problems, the individuals are split i...
We study the reinforcement learning for finite-horizon episodic Markov
d...
We study reinforcement learning for two-player zero-sum Markov games wit...
We study reinforcement learning in an infinite-horizon average-reward se...
We study reinforcement learning (RL) with linear function approximation ...
We study reinforcement learning (RL) with linear function approximation ...
Reinforcement learning (RL) with linear function approximation has recei...
Multi-objective reinforcement learning (MORL) is an extension of ordinar...
Thompson Sampling (TS) is one of the most effective algorithms for solvi...
We study the reinforcement learning problem for discounted Markov Decisi...
Modern tasks in reinforcement learning are always with large state and a...
We study the stochastic contextual bandit problem, where the reward is
g...
Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have
...
Smooth finite-sum optimization has been widely studied in both convex an...
We propose a sample efficient stochastic variance-reduced cubic
regulari...
We study the problem of training deep neural networks with Rectified Lin...
Adaptive gradient methods are workhorses in deep learning. However, the
...
We propose two algorithms that can find local minima faster than the
sta...
We study finite-sum nonconvex optimization problems, where the objective...
We propose a stochastic variance-reduced cubic regularized Newton method...