Nicolas Heess

A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning AISTATS 2025 EvoControl: Multi-Frequency Bi-Level Control for High-Frequency Continuous Control ICML 2025 Re-evaluating Open-ended Evaluation of Large Language Models ICLR 2025 Learning from negative feedback, or positive feedback or both ICLR 2025 Learning-Order Autoregressive Models with Application to Molecular Graph Generation ICML 2025 PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs ICML 2024 Genie: Generative Interactive Environments ICML 2024 Replay across Experiments: A Natural Extension of Off-Policy RL ICLR 2024 Learning to Learn Faster from Human Feedback with Language Model Predictive Control RSS 2024 NfgTransformer: Equivariant Representation Learning for Normal-form Games ICLR 2024 The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models ACL 2024 Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning CORL 2024 Offline Actor-Critic Reinforcement Learning Scales to Large Models ICML 2024 Language to Rewards for Robotic Skill Synthesis CORL 2023 Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation ICLR 2023 Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning ICLR 2023 Representation Learning in Deep RL via Discrete Information Bottleneck AISTATS 2023 Coherent Soft Imitation Learning NIPS 2023 Retrieval-Augmented Reinforcement Learning ICML 2022 Data augmentation for efficient learning from parametric experts NIPS 2022 NeuPL: Neural Population Learning ICLR 2022 Evaluating Model-Based Planning and Planner Amortization for Continuous Control ICLR 2022 COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation ICLR 2022 Learning transferable motor skills with hierarchical latent mixture policies ICLR 2022 Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games ICML 2022 Behavior Priors for Efficient Reinforcement Learning JMLR 2022 Neural Production Systems NIPS 2021 Counterfactual Credit Assignment in Model-Free Reinforcement Learning ICML 2021 Data-efficient Hindsight Off-policy Option Learning ICML 2021 Entropic Desired Dynamics for Intrinsic Control NIPS 2021 A Constrained Multi-Objective Reinforcement Learning Framework CORL 2021 Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion CORL 2021 Collect & Infer - a fresh look at data-efficient Reinforcement Learning CORL 2021 Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions AISTATS 2020 RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning NIPS 2020 Stabilizing Transformers for Reinforcement Learning ICML 2020 CoMic: Complementary Task Learning & Mimicry for Reusable Skills ICML 2020 A distributional view on multi-objective policy optimization ICML 2020 Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces NIPS 2020 Value-driven Hindsight Modelling NIPS 2020 Critic Regularized Regression NIPS 2020 Learning Dexterous Manipulation from Suboptimal Experts CORL 2020 Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning ICLR 2020 A Generalized Training Approach for Multiagent Learning ICLR 2020 V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control ICLR 2020 Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion CORL 2020 Compositional Transfer in Hierarchical Reinforcement Learning RSS 2020 Hierarchical Visuomotor Control of Humanoids ICLR 2019 The Termination Critic AISTATS 2019 Credit Assignment Techniques in Stochastic Computation Graphs AISTATS 2019 Composing Entropic Policies using Divergence Correction ICML 2019 Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics CORL 2019 Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models CORL 2019 Hindsight Credit Assignment NIPS 2019 Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures ICLR 2019 Information asymmetry in KL-regularized RL ICLR 2019 Neural Probabilistic Motor Primitives for Humanoid Control ICLR 2019 Emergent Coordination Through Competition ICLR 2019 Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search ICLR 2019 Reinforcement and Imitation Learning for Diverse Visuomotor Skills RSS 2018 Maximum a Posteriori Policy Optimisation ICLR 2018 Distributed Distributional Deterministic Policy Gradients ICLR 2018 Learning an Embedding Space for Transferable Robot Skills ICLR 2018 Mix & Match Agent Curricula for Reinforcement Learning ICML 2018 Learning by Playing Solving Sparse Reward Tasks from Scratch ICML 2018 Graph Networks as Learnable Physics Engines for Inference and Control ICML 2018 Imagination-Augmented Agents for Deep Reinforcement Learning NIPS 2017 FeUdal Networks for Hierarchical Reinforcement Learning ICML 2017 Sim-to-Real Robot Learning from Pixels with Progressive Nets CORL 2017 Robust Imitation of Diverse Behaviors NIPS 2017 Distral: Robust multitask reinforcement learning NIPS 2017 Learning Hierarchical Information Flow with Recurrent Neural Modules NIPS 2017 Filtering Variational Objectives NIPS 2017 Unsupervised Learning of 3D Structure from Images NIPS 2016 Attend, Infer, Repeat: Fast Scene Understanding with Generative Models NIPS 2016 Learning Continuous Control Policies by Stochastic Value Gradients NIPS 2015 Gradient Estimation Using Stochastic Computation Graphs NIPS 2015 Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection AISTATS 2014 Recurrent Models of Visual Attention NIPS 2014 Bayes-Adaptive Simulation-based Search with Value Function Approximation NIPS 2014 Deterministic Policy Gradient Algorithms ICML 2014 Learning to Pass Expectation Propagation Messages NIPS 2013 Searching for objects driven by context NIPS 2012