PIPER: Primitive-Informed Preference-based Hierarchical Reinforcement Learning via Hindsight Relabeling

no code implementations • 20 Apr 2024 • Utsav Singh, Wesley A. Suttle, Brian M. Sadler, Vinay P. Namboodiri, Amrit Singh Bedi

In this work, we introduce PIPER: Primitive-Informed Preference-based Hierarchical reinforcement learning via Hindsight Relabeling, a novel approach that leverages preference-based learning to learn a reward model, and subsequently uses this reward model to relabel higher-level replay buffers.

Hierarchical Reinforcement Learning reinforcement-learning

PEAR: Primitive enabled Adaptive Relabeling for boosting Hierarchical Reinforcement Learning

no code implementations • 10 Jun 2023 • Utsav Singh, Vinay P. Namboodiri

Hierarchical reinforcement learning (HRL) has the potential to solve complex long horizon tasks using temporal abstraction and increased exploration.

Hierarchical Reinforcement Learning Imitation Learning +2

CRISP: Curriculum inducing Primitive Informed Subgoal Prediction

no code implementations • 7 Apr 2023 • Utsav Singh, Vinay P. Namboodiri

Hierarchical reinforcement learning (HRL) is a promising approach that uses temporal abstraction to solve complex long horizon problems.

Hierarchical Reinforcement Learning Imitation Learning +1

InfoRL: Interpretable Reinforcement Learning using Information Maximization

no code implementations • 24 May 2019 • Aadil Hayat, Utsav Singh, Vinay P. Namboodiri

Recent advances in reinforcement learning have proved that given an environment we can learn to perform a task in that environment if we have access to some form of a reward function (dense, sparse or derived from IRL).

reinforcement-learning Reinforcement Learning (RL)