← Authors

Nicolas Heess

83 papers · 2012–2025 · 8 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (21) 🐝 Cross-Pollinator (10) πŸƒ Academic Marathon (13) 🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌟 Keyword Trendsetter Combo (7) 🏠 Conference Loyalist (22) 🀝 Dynamic Duo (21) πŸ‘₯ Mega-Team (49) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (25) πŸ† Keyword Champion 🧬 Topic Evolution πŸ”₯ Unstoppable (14) πŸ“ˆ Trend Setter πŸ’Ž Century Club (83) πŸš€ Conference Pioneer ⚑ Prolific Year (8) πŸ—ƒοΈ Keyword Collector (78)

Conferences

ICLR (22) NIPS (22) ICML (18) CORL (10) AISTATS (6) RSS (3) ACL (1) JMLR (1)

Top co-authors

Martin Riedmiller (21) Jost Tobias Springenberg (14) Abbas Abdolmaleki (12) Leonard Hasenclever (12) Roland Hafner (10) Dhruva Tirumala (9) Thomas Lampe (9) Josh Merel (9) Michael Neunert (9) Raia Hadsell (9)

Keywords

reinforcement learning (14) deep reinforcement learning (6) value function (6) off-policy learning (5) transfer learning (5) offline reinforcement learning (5) multi-task learning (4) imitation learning (4) continuous control (4) policy optimization (4) variance reduction (4) credit assignment (4) representation learning (3) hierarchical reinforcement learning (3) probabilistic inference (3) robot manipulation (3) sim-to-real transfer (3) policy gradient (3) variational inference (3) model-based reinforcement learning (3)

Papers

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