Discretization-based approaches to solving online reinforcement learning problems are
studied extensively on applications such as resource allocation and cache management …

In this work, we propose KeRNS: an algorithm for episodic reinforcement learning in non-
stationary Markov Decision Processes (MDPs) whose state-action set is endowed with a …

Reinforcement learning algorithms often require finiteness of state and action spaces in
Markov decision processes (MDPs)(also called controlled Markov chains) and various …

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 …

In this paper, we study Lipschitz bandit problems with batched feedback, where the
expected reward is Lipschitz and the reward observations are communicated to the player in …

Plain reinforcement learning (RL) may be prone to loss of convergence, constraint violation,
unexpected performance, etc. Commonly, RL agents undergo extensive learning stages to …

Sample-efficiency and reliability remain major bottlenecks toward wide adoption of
reinforcement learning algorithms in continuous settings with high-dimensional perceptual …

Language-conditioned robotic learning has significantly enhanced robot adaptability by
enabling a single model to execute diverse tasks in response to verbal commands. Despite …