Amy Zhang
Assistant Professor
Works on sample-efficient and generalizable reinforcement learning, representation learning and decision making for robotics.
Amy Zhang's lab at UT Austin works on reinforcement learning, generative & foundation models and efficient training & inference. 29 papers since 2023, 18 at top venues, mostly ICLR and NeurIPS. 16 PhD students. 3 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 62%
- PhD students
- 16
- MS students
- 0
- alumni listed
- 3
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 5 · collecting
- 12 papers2025 · T1 6
- 10 papers2024 · T1 6
- 1 papers2023 · T1 1
T1 venuesOtherVenues: ICLR, NeurIPS, CoRL, RSS
Research focuspaper titles that use each topic's words
- Reinforcement learning15 papers
- Generative & foundation models2 papers
- Efficient training & inference2 papers
- sample-efficient0 papers
- representation learning0 papers
Paper list
From the lab homepage.
20266 papers
- NeurIPS Position TrackBeyond Training Time, Test-Time Coordination is Essential for Cooperative MARL (opens in a new tab)
- ICLR (T1)Multi-agent Coordination via Flow Matching (opens in a new tab)
- ICLR (T1)Reevaluating Policy Gradient Methods for Imperfect-Information Games (opens in a new tab)
- ICLR (T1)Reinforcement Learning via Value Gradient Flow (opens in a new tab)
- ICLR (T1)Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning (opens in a new tab)
- NeurIPS (T1)Test-time Multi-agent Coordination by Decomposed Value Gradient Flow (opens in a new tab)
202512 papers
- ICLR (T1)An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning (opens in a new tab)
- ICLR SynthData WorkshopAugmented Conditioning Is Enough For Effective Training Image Generation (opens in a new tab)
- RSS (T1)CRESTE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance (opens in a new tab)
- ICLR (T1)EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation (opens in a new tab)
- RLCFast Adaptation with Behavioral Foundation Models (opens in a new tab)
- NeurIPS (T1)Information-Theoretic Reward Decomposition for Generalizable RLHF (opens in a new tab)
- ICLR (T1)Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory (opens in a new tab)
- RLCOffline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets (opens in a new tab)
- —Proto Successor Measure: Representing the Behavior Space of an RL Agent (opens in a new tab)
- ICLR Robot Learning WorkshopRL Zero: Zero-Shot Language to Behaviors without any Supervision (opens in a new tab)
- NeurIPS (T1)Uni-RL: Unifying Online and Offline RL via Implicit Value Regularization (opens in a new tab)
- NeurIPS ARLET WorkshopUnifying Agent Interaction and World Information for Multi-agent Coordination (opens in a new tab)
202410 papers
- CoRL (T1)A Dual Approach to Imitation Learning from Observations with Offline Datasets (opens in a new tab)
- NeurIPS (T1)AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers (opens in a new tab)
- —Automated Discovery of Functional Actual Causes in Complex Environments (opens in a new tab)
- NeurIPS (T1)Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning (opens in a new tab)
- ICLR (T1)Dual RL: Unification and New Methods for Reinforcement and Imitation Learning (opens in a new tab)
- RLCLearning Action-based Representations Using Invariance (opens in a new tab)
- RLCMultistep Inverse Is Not All You Need (opens in a new tab)
- ICRA WorkshopRobot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning (opens in a new tab)
- ICLR (T1)Score Models for Offline Goal-Conditioned Reinforcement Learning (opens in a new tab)
- NeurIPS (T1)SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions (opens in a new tab)
Where alumni went3 listed
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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