Author Info
Name
Affiliation
Papers
NICOLAS HEESS
Neuroinformatics and Computational Neuroscience Doctoral Training Centre, Institute for Adaptive and Neural Computation, School of lnformatics, University of Edinburgh, Edinburgh, U.K.
91
Collaborators
Citations 
PageRank 
322
1762
94.77
Referers 
Referees 
References 
4489
1390
926
Search Limit
1001000
Title
Citations
PageRank
Year
Retrieval-Augmented Reinforcement Learning.00.342022
Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games.00.342022
Evaluating Model-Based Planning and Planner Amortization for Continuous Control00.342022
NeuPL: Neural Population Learning00.342022
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation00.342022
Offline Meta-Reinforcement Learning for Industrial Insertion00.342022
From motor control to team play in simulated humanoid football.00.342022
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner.00.342022
Learning transferable motor skills with hierarchical latent mixture policies00.342022
Data-efficient Hindsight Off-policy Option Learning00.342021
Neural Production Systems.00.342021
Game Plan: What AI can do for Football, and What Football can do for AI10.352021
Counterfactual Credit Assignment in Model-Free Reinforcement Learning00.342021
A Constrained Multi-Objective Reinforcement Learning Framework.00.342021
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning00.342020
Towards General and Autonomous Learning of Core Skills - A Case Study in Locomotion.00.342020
Stabilizing Transformers for Reinforcement Learning00.342020
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions00.342020
V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control00.342020
Critic Regularized Regression00.342020
A Generalized Training Approach for Multiagent Learning00.342020
Learning Dexterous Manipulation from Suboptimal Experts.00.342020
Catch & Carry: reusable neural controllers for vision-guided whole-body tasks10.352020
CoMic - Complementary Task Learning & Mimicry for Reusable Skills.00.342020
Value-driven Hindsight Modelling00.342020
Compositional Transfer in Hierarchical Reinforcement Learning00.342020
A distributional view on multi-objective policy optimization.00.342020
Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning00.342020
The Body is Not a Given: Joint Agent Policy Learning and Morphology Evolution.10.342019
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL.20.362019
Value constrained model-free continuous control.00.342019
Emergent Coordination Through Competition.40.372019
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models00.342019
Composing Entropic Policies using Divergence Correction.00.342019
Information asymmetry in KL-regularized RL.10.352019
Observational Learning by Reinforcement Learning00.342019
Meta-learning of Sequential Strategies.20.362019
Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics00.342019
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces00.342019
Hindsight Credit Assignment.00.342019
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search00.342019
Meta reinforcement learning as task inference.10.352019
Self-supervised Learning of Image Embedding for Continuous Control.30.372019
Regularized Hierarchical Policies for Compositional Transfer in Robotics.00.342019
Relative Entropy Regularized Policy Iteration.20.362018
Learning an Embedding Space for Transferable Robot Skills200.632018
Distributed Distributional Deterministic Policy Gradients.200.702018
Graph Networks as Learnable Physics Engines for Inference and Control.230.692018
Hierarchical visuomotor control of humanoids.60.412018
Relational inductive biases, deep learning, and graph networks.741.412018
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