111 citations · 229 across the 16 of their papers we have counts for
10 papers · 1 filter
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…
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…
Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul +3
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable imp…
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang +1
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimizati…
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos +1
Natural agents can effectively learn from multiple data sources that differ in size, quality, and types of measurements. We study this heterogeneity in the context of offline reinf…
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et a…
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio +3
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilisti…
Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches s…
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Qinqing Zheng, Bor-Yiing Su, Jiyan Yang +7
Recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time. While the training throughput can…
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Qinqing Zheng, Ryota Tomioka
We consider the problem of recovering a low-rank tensor from its noisy observation. Previous work has shown a recovery guarantee with signal to noise ratio $O(n^{\lceil K/2 \rceil…