53 citations · 147 across the 20 of their papers we have counts for
20 papers
Visual Pre-Training on Unlabeled Images using Reinforcement Learning
Dibya Ghosh, Sergey Levine
In reinforcement learning (RL), value-based algorithms learn to associate each observation with the states and rewards that are likely to be reached from it. We observe that many s…
: a Vision-Language-Action Model with Open-World Generalization
Physical Intelligence, Kevin Black, Noah Brown +33
In order for robots to be useful, they must perform practically relevant tasks in the real world, outside of the lab. While vision-language-action (VLA) models have demonstrated im…
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data
Nitish Dashora, Dibya Ghosh, Sergey Levine
Online reinforcement learning (RL) with sparse rewards poses a challenge partly because of the lack of feedback on states leading to the goal. Furthermore, expert offline data with…
What Do Learning Dynamics Reveal About Generalization in LLM Reasoning?
Katie Kang, Amrith Setlur, Dibya Ghosh +4
Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better und…
Octo: An Open-Source Generalist Robot Policy
Octo Model Team, Dibya Ghosh, Homer Walke +16
Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies…
Accelerating Exploration with Unlabeled Prior Data
Qiyang Li, Jason Zhang, Dibya Ghosh +2
Learning to solve tasks from a sparse reward signal is a major challenge for standard reinforcement learning (RL) algorithms. However, in the real world, agents rarely need to solv…
Robotic Offline RL from Internet Videos via Value-Function Pre-Training
Chethan Bhateja, Derek Guo, Dibya Ghosh +6
Pre-training on Internet data has proven to be a key ingredient for broad generalization in many modern ML systems. What would it take to enable such capabilities in robotic reinfo…
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
Seohong Park, Dibya Ghosh, Benjamin Eysenbach +1
Unsupervised pre-training has recently become the bedrock for computer vision and natural language processing. In reinforcement learning (RL), goal-conditioned RL can potentially p…
Reinforcement Learning from Passive Data via Latent Intentions
Dibya Ghosh, Chethan Bhateja, Sergey Levine
Passive observational data, such as human videos, is abundant and rich in information, yet remains largely untapped by current RL methods. Perhaps surprisingly, we show that passiv…
Distributionally Adaptive Meta Reinforcement Learning
Anurag Ajay, Abhishek Gupta, Dibya Ghosh +2
Meta-reinforcement learning algorithms provide a data-driven way to acquire policies that quickly adapt to many tasks with varying rewards or dynamics functions. However, learned m…
Offline RL Policies Should be Trained to be Adaptive
Dibya Ghosh, Anurag Ajay, Pulkit Agrawal +1
Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge…
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
Dibya Ghosh, Jad Rahme, Aviral Kumar +3
Generalization is a central challenge for the deployment of reinforcement learning (RL) systems in the real world. In this paper, we show that the sequential structure of the RL pr…
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
Aviral Kumar, Rishabh Agarwal, Dibya Ghosh +1
We identify an implicit under-parameterization phenomenon in value-based deep RL methods that use bootstrapping: when value functions, approximated using deep neural networks, are…
Representations for Stable Off-Policy Reinforcement Learning
Dibya Ghosh, Marc G. Bellemare
Reinforcement learning with function approximation can be unstable and even divergent, especially when combined with off-policy learning and Bellman updates. In deep reinforcement…
An operator view of policy gradient methods
Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux
We cast policy gradient methods as the repeated application of two operators: a policy improvement operator , which maps any policy to a better one ,…
On Catastrophic Interference in Atari 2600 Games
William Fedus, Dibya Ghosh, John D. Martin +3
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interference within an environment inhibits le…
Learning to Reach Goals via Iterated Supervised Learning
Dibya Ghosh, Abhishek Gupta, Ashwin Reddy +4
Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards. Although supervised imitat…
Learning Actionable Representations with Goal-Conditioned Policies
Dibya Ghosh, Abhishek Gupta, Sergey Levine
Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to t…
Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition
Justin Fu, Avi Singh, Dibya Ghosh +2
The design of a reward function often poses a major practical challenge to real-world applications of reinforcement learning. Approaches such as inverse reinforcement learning atte…
Divide-and-Conquer Reinforcement Learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran +2
Standard model-free deep reinforcement learning (RL) algorithms sample a new initial state for each trial, allowing them to optimize policies that can perform well even in highly s…