257 citations · 1k across the 96 of their papers we have counts for
18 papers · 1 filter
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Martin Klissarov, Mikael Henaff, Roberta Raileanu +7
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system. We present MaestroMotif, a m…
EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation
Carl Qi, Dan Haramati, Tal Daniel +2
Object manipulation is a common component of everyday tasks, but learning to manipulate objects from high-dimensional observations presents significant challenges. These challenges…
RLZero: Direct Policy Inference from Language Without In-Domain Supervision
Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo +6
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward sign…
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
Jake Grigsby, Justin Sasek, Samyak Parajuli +3
Language models trained on diverse datasets unlock generalization by in-context learning. Reinforcement Learning (RL) policies can achieve a similar effect by meta-learning within…
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Siddhant Agarwal, Harshit Sikchi, Peter Stone +1
Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Refe…
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck +5
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised…
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Qinqing Zheng, Mikael Henaff, Amy Zhang +2
Automatically synthesizing dense rewards from natural language descriptions is a promising paradigm in reinforcement learning (RL), with applications to sparse reward problems, ope…
Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory
Alexander Levine, Peter Stone, Amy Zhang
In order to train agents that can quickly adapt to new objectives or reward functions, efficient unsupervised representation learning in sequential decision-making environments can…
TRAM: Test-Time Risk Adaptation with Mixture of Agents
Mohamad Fares El Hajj Chehade, Amrit Singh Bedi, Amy Zhang +1
Deployed reinforcement learning agents often face safety requirements that are specified only after training, such as new hazard maps, revised risk thresholds, or behavioral alignm…
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
Liyuan Mao, Haoran Xu, Xianyuan Zhan +2
One important property of DIstribution Correction Estimation (DICE) methods is that the solution is the optimal stationary distribution ratio between the optimized and data collect…
Unified Auto-Encoding with Masked Diffusion
Philippe Hansen-Estruch, Sriram Vishwanath, Amy Zhang +1
At the core of both successful generative and self-supervised representation learning models there is a reconstruction objective that incorporates some form of image corruption. Di…
A Dual Approach to Imitation Learning from Observations with Offline Datasets
Harshit Sikchi, Caleb Chuck, Amy Zhang +1
Demonstrations are an effective alternative to task specification for learning agents in settings where designing a reward function is difficult. However, demonstrating expert beha…
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje +13
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might f…
Automated Discovery of Functional Actual Causes in Complex Environments
Caleb Chuck, Sankaran Vaidyanathan, Stephen Giguere +3
Reinforcement learning (RL) algorithms often struggle to learn policies that generalize to novel situations due to issues such as causal confusion, overfitting to irrelevant factor…
Learning Action-based Representations Using Invariance
Max Rudolph, Caleb Chuck, Kevin Black +3
Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors. A representation that…
Multistep Inverse Is Not All You Need
Alexander Levine, Peter Stone, Amy Zhang
In real-world control settings, the observation space is often unnecessarily high-dimensional and subject to time-correlated noise. However, the controllable dynamics of the system…
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
Zihan Ding, Amy Zhang, Yuandong Tian +1
We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step d…
Zero-Shot Reinforcement Learning via Function Encoders
Tyler Ingebrand, Amy Zhang, Ufuk Topcu
Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The diff…