307 citations · 626 across the 12 of their papers we have counts for
8 papers · 1 filter
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks
Murtaza Dalal, Tarun Chiruvolu, Devendra Chaplot +1
Large Language Models (LLMs) have been shown to be capable of performing high-level planning for long-horizon robotics tasks, yet existing methods require access to a pre-defined s…
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives
Murtaza Dalal, Deepak Pathak, Ruslan Salakhutdinov
Despite the potential of reinforcement learning (RL) for building general-purpose robotic systems, training RL agents to solve robotics tasks still remains challenging due to the d…
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
Ashvin Nair, Abhishek Gupta, Murtaza Dalal +1
Reinforcement learning (RL) provides an appealing formalism for learning control policies from experience. However, the classic active formulation of RL necessitates a lengthy acti…
Autoregressive Models: What Are They Good For?
Murtaza Dalal, Alexander C. Li, Rohan Taori
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates. We investigate the use of AR models as density…
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Vitchyr H. Pong, Murtaza Dalal, Steven Lin +3
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward f…
Visual Reinforcement Learning with Imagined Goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal +3
For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be able to learn broadly applicable and general-purpose skill repertoires. Furthermore…
Composable Deep Reinforcement Learning for Robotic Manipulation
Tuomas Haarnoja, Vitchyr Pong, Aurick Zhou +3
Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, mod…
Temporal Difference Models: Model-Free Deep RL for Model-Based Control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal +1
Model-free reinforcement learning (RL) is a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challengi…