Learning robotic navigation from experience: principles, methods and recent results
Navigation is one of the most heavily studied problems in robotics and is conventionally
approached as a geometric mapping and planning problem. However, real-world navigation …
approached as a geometric mapping and planning problem. However, real-world navigation …
Goal-conditioned imitation learning using score-based diffusion policies
We propose a new policy representation based on score-based diffusion models (SDMs).
We apply our new policy representation in the domain of Goal-Conditioned Imitation …
We apply our new policy representation in the domain of Goal-Conditioned Imitation …
Hiql: Offline goal-conditioned rl with latent states as actions
Unsupervised pre-training has recently become the bedrock for computer vision and natural
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …
language processing. In reinforcement learning (RL), goal-conditioned RL can potentially …
Rorl: Robust offline reinforcement learning via conservative smoothing
Offline reinforcement learning (RL) provides a promising direction to exploit massive amount
of offline data for complex decision-making tasks. Due to the distribution shift issue, current …
of offline data for complex decision-making tasks. Due to the distribution shift issue, current …
A policy-guided imitation approach for offline reinforcement learning
Offline reinforcement learning (RL) methods can generally be categorized into two types: RL-
based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution …
based and Imitation-based. RL-based methods could in principle enjoy out-of-distribution …
Hierarchical diffusion for offline decision making
Offline reinforcement learning typically introduces a hierarchical structure to solve the long-
horizon problem so as to address its thorny issue of variance accumulation. Problems of …
horizon problem so as to address its thorny issue of variance accumulation. Problems of …
From play to policy: Conditional behavior generation from uncurated robot data
While large-scale sequence modeling from offline data has led to impressive performance
gains in natural language and image generation, directly translating such ideas to robotics …
gains in natural language and image generation, directly translating such ideas to robotics …
Design from policies: Conservative test-time adaptation for offline policy optimization
In this work, we decouple the iterative bi-level offline RL (value estimation and policy
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …
extraction) from the offline training phase, forming a non-iterative bi-level paradigm and …
Exploit reward shifting in value-based deep-rl: Optimistic curiosity-based exploration and conservative exploitation via linear reward shaping
In this work, we study the simple yet universally applicable case of reward shaping in value-
based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of a …
based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of a …
Offline Goal-Conditioned Reinforcement Learning via -Advantage Regression
JY Ma, J Yan, D Jayaraman… - Advances in neural …, 2022 - proceedings.neurips.cc
Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill
learning in the form of reaching diverse goals from purely offline datasets. We propose …
learning in the form of reaching diverse goals from purely offline datasets. We propose …