Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

We consider the question of learning $ Q $-function in a sample efficient manner for
reinforcement learning with continuous state and action spaces under a generative model. If …

In this work, we consider the popular tree-based search strategy within the framework of
reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of infinite …

The practicality of reinforcement learning algorithms has been limited due to poor scaling
with respect to the problem size, as the sample complexity of learning an ε-optimal policy is …

While advances in multi-agent learning have enabled the training of increasingly complex
agents, most existing techniques produce a final policy that is not designed to adapt to a …

Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from
new experience. Despite being empirically observed in several problem settings, little is …

Reinforcement Learning (RL), bolstered by the expressive capabilities of Deep Neural
Networks (DNNs) for function approximation, has demonstrated considerable success in …

We show that deep reinforcement learning can maintain its ability to learn without resetting
network parameters in settings where the number of gradient updates greatly exceeds the …

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge
concept recommendation are the three major steps that systematically instruct users to learn …

Value function (VF) approximation is a central problem in reinforcement learning (RL).
Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result …