We consider the problem of preference-based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit …

Realistic environments often provide agents with very limited feedback. When the
environment is initially unknown, the feedback, in the beginning, can be completely absent …

Abstract Model-based reinforcement learning algorithms with probabilistic dynamical
models are amongst the most data-efficient learning methods. This is often attributed to their …

We consider the problem of preference based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/1) …

Reinforcement Learning (RL) algorithms suffer from the dependency on accurately
engineered reward functions to properly guide the learning agents to do the required tasks …

We address the challenge of exploration in reinforcement learning (RL) when the agent
operates in an unknown environment with sparse or no rewards. In this work, we study the …

Abstract We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new
algorithm for reinforcement learning in tabular and possibly stage-dependent, episodic …

Function approximation has been an indispensable component in modern reinforcement
learning algorithms designed to tackle problems with large state spaces in high dimensions …

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

We consider reinforcement learning in an environment modeled by an episodic, tabular,
step-dependent Markov decision process of horizon $ H $ with $ S $ states, and $ A …