Nicolas Heess
83 papers · 2012–2025 · 8 venues · across top CS/AI venues
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Achievements
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(8)
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(21)
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(7)
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Research topics
Reinforcement Learning
(57)
Machine Learning
(52)
Artificial Intelligence
(51)
Methods
(49)
Core AI
(42)
Learning Types Β· Machine Learning
(30)
Deep Learning
(26)
Applications
(22)
Optimization & Theory
(19)
Learning Types Β· Deep Learning
(16)
Core Methods
(13)
Learning Paradigms
(13)
Robotics
(8)
Architectures
(8)
Bayesian & Probabilistic
(8)
Keywords
reinforcement learning
(19)
continuous control
(9)
transfer learning
(9)
off-policy learning
(8)
value function
(8)
deep reinforcement learning
(8)
policy optimization
(7)
offline reinforcement learning
(6)
representation learning
(5)
imitation learning
(5)
neural network
(4)
credit assignment
(4)
game theory
(4)
multi-task learning
(4)
hierarchical reinforcement learning
(4)
variance reduction
(4)
variational inference
(4)
model-based reinforcement learning
(4)
sequential decision making
(3)
policy gradient
(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
Learning from negative feedback, or positive feedback or both
ICLR 2025
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
ICML 2025
Re-evaluating Open-ended Evaluation of Large Language Models
ICLR 2025
Genie: Generative Interactive Environments
ICML 2024
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
CoRL 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
Offline Actor-Critic Reinforcement Learning Scales to Large Models
ICML 2024
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs
ICML 2024
Replay across Experiments: A Natural Extension of Off-Policy RL
ICLR 2024
The Probabilities Also Matter: A More Faithful Metric for Faithfulness of Free-Text Explanations in Large Language Models
ACL 2024
Coherent Soft Imitation Learning
NeurIPS 2023
Language to Rewards for Robotic Skill Synthesis
CoRL 2023
Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation
ICLR 2023
Representation Learning in Deep RL via Discrete Information Bottleneck
AISTATS 2023
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
ICLR 2023
Behavior Priors for Efficient Reinforcement Learning
JMLR 2022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
ICLR 2022
Data augmentation for efficient learning from parametric experts
NeurIPS 2022
Evaluating Model-Based Planning and Planner Amortization for Continuous Control
ICLR 2022
Learning transferable motor skills with hierarchical latent mixture policies
ICLR 2022
NeuPL: Neural Population Learning
ICLR 2022
Retrieval-Augmented Reinforcement Learning
ICML 2022
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games
ICML 2022
A Constrained Multi-Objective Reinforcement Learning Framework
CoRL 2021
Collect & Infer - a fresh look at data-efficient Reinforcement Learning
CoRL 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
NeurIPS 2021
Neural Production Systems
NeurIPS 2021
Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion
CoRL 2021
A distributional view on multi-objective policy optimization
ICML 2020
A Generalized Training Approach for Multiagent Learning
ICLR 2020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions
AISTATS 2020
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
ICML 2020
Compositional Transfer in Hierarchical Reinforcement Learning
RSS 2020
Critic Regularized Regression
NeurIPS 2020
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces
NeurIPS 2020
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning
ICLR 2020
Learning Dexterous Manipulation from Suboptimal Experts
CoRL 2020
RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning
NeurIPS 2020
Stabilizing Transformers for Reinforcement Learning
ICML 2020
Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion
CoRL 2020
Value-driven Hindsight Modelling
NeurIPS 2020
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control
ICLR 2020
Composing Entropic Policies using Divergence Correction
ICML 2019
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics
CoRL 2019
Credit Assignment Techniques in Stochastic Computation Graphs
AISTATS 2019
Emergent Coordination Through Competition
ICLR 2019
Hierarchical Visuomotor Control of Humanoids
ICLR 2019
Hindsight Credit Assignment
NeurIPS 2019
Imagined Value Gradients: Model-Based Policy Optimization with Tranferable Latent Dynamics Models
CoRL 2019
Information asymmetry in KL-regularized RL
ICLR 2019
Neural Probabilistic Motor Primitives for Humanoid Control
ICLR 2019
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
ICLR 2019
The Termination Critic
AISTATS 2019
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
ICLR 2019
Distributed Distributional Deterministic Policy Gradients
ICLR 2018
Graph Networks as Learnable Physics Engines for Inference and Control
ICML 2018
Learning an Embedding Space for Transferable Robot Skills
ICLR 2018
Learning by Playing Solving Sparse Reward Tasks from Scratch
ICML 2018
Maximum a Posteriori Policy Optimisation
ICLR 2018
Mix & Match Agent Curricula for Reinforcement Learning
ICML 2018
Reinforcement and Imitation Learning for Diverse Visuomotor Skills
RSS 2018
Distral: Robust multitask reinforcement learning
NeurIPS 2017
FeUdal Networks for Hierarchical Reinforcement Learning
ICML 2017
Filtering Variational Objectives
NeurIPS 2017
Imagination-Augmented Agents for Deep Reinforcement Learning
NeurIPS 2017
Learning Hierarchical Information Flow with Recurrent Neural Modules
NeurIPS 2017
Robust Imitation of Diverse Behaviors
NeurIPS 2017
Sim-to-Real Robot Learning from Pixels with Progressive Nets
CoRL 2017
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
NeurIPS 2016
Unsupervised Learning of 3D Structure from Images
NeurIPS 2016
Gradient Estimation Using Stochastic Computation Graphs
NeurIPS 2015
Learning Continuous Control Policies by Stochastic Value Gradients
NeurIPS 2015
Bayes-Adaptive Simulation-based Search with Value Function Approximation
NeurIPS 2014
Deterministic Policy Gradient Algorithms
ICML 2014
Recurrent Models of Visual Attention
NeurIPS 2014
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
AISTATS 2014
Learning to Pass Expectation Propagation Messages
NeurIPS 2013
Searching for objects driven by context
NeurIPS 2012