← Authors

Matteo Hessel

21 papers · 2016–2022 · 4 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌍 Conference Polyglot (4) πŸ—ΊοΈ Taxonomy Completionist (26) 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (6) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown πŸ† Grand Slam πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (2) πŸ”₯ Unstoppable (7) ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (75) ❓ The Questioner (2) πŸ’Ž Century Club (21) πŸš€ Conference Pioneer

Conferences

NIPS (8) ICML (7) ICLR (4) AAAI (2)

Top co-authors

David Silver (15) Hado van Hasselt (8) Hado P van Hasselt (8) Zhongwen Xu (7) Junhyuk Oh (6) Satinder P. Singh (4) Andre Barreto (3) Satinder Singh (3) Vivek Veeriah (3) Tom Schaul (3)

Research topics

Reinforcement Learning (1)

Keywords

reinforcement learning (8) deep reinforcement learning (4) policy gradient (3) atari game (3) value function (3) off-policy learning (3) model-based reinforcement learning (3) experience replay (2) credit assignment (2) value function estimation (2) function approximation (2) temporal difference learning (2) reward function (2) auxiliary task (2) temporal-difference learning (2) successor feature (2) value iteration (2) hierarchical reinforcement learning (1) deep learning (1) multi-task learning (1)

Papers

Learning by Directional Gradient Descent ICLR 2022
Muesli: Combining Improvements in Policy Optimization ICML 2021
Self-Consistent Models and Values NIPS 2021
Discovery of Options via Meta-Learned Subgoals NIPS 2021
Expected Eligibility Traces AAAI 2021
Emphatic Algorithms for Deep Reinforcement Learning ICML 2021
A Self-Tuning Actor-Critic Algorithm NIPS 2020
Behaviour Suite for Reinforcement Learning ICLR 2020
Off-Policy Actor-Critic with Shared Experience Replay ICML 2020
Discovering Reinforcement Learning Algorithms NIPS 2020
Meta-Gradient Reinforcement Learning with an Objective Discovered Online NIPS 2020
What Can Learned Intrinsic Rewards Capture? ICML 2020
Multi-Task Deep Reinforcement Learning with PopArt AAAI 2019
When to use parametric models in reinforcement learning? NIPS 2019
Discovery of Useful Questions as Auxiliary Tasks NIPS 2019
Noisy Networks For Exploration ICLR 2018
Distributed Prioritized Experience Replay ICLR 2018
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement ICML 2018
The Predictron: End-To-End Learning and Planning ICML 2017
Dueling Network Architectures for Deep Reinforcement Learning ICML 2016
Learning values across many orders of magnitude NIPS 2016