We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where
instead of receiving a numeric reward at each step, the RL agent only receives preferences …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

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 …

Convex Reinforcement Learning (RL) is a recently introduced framework that generalizes
the standard RL objective to any convex (or concave) function of the state distribution …

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 algorithms that learn from human feedback (RLHF) need to be
efficient in terms of statistical complexity, computational complexity, and query complexity. In …

The interactions an animal has with its prey, predators, neighbors, and competitors are
known as ecological interactions. Making effective decisions during ecological interactions …

In reinforcement learning (RL), rewards of states are typically considered additive, and
following the Markov assumption, they are $\textit {independent} $ of states visited …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Abstract The classic Reinforcement Learning (RL) formulation concerns the maximization of
a scalar reward function. More recently, convex RL has been introduced to extend the RL …