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← Methods
Reinforcement Learning
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Methods
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Deep RL
3,861 papers
Papers per year
2005: 1
2006: 9
2007: 14
2008: 15
2009: 9
2010: 21
2011: 27
2012: 32
2013: 21
2014: 17
2015: 10
2016: 33
2017: 102
2018: 222
2019: 399
2020: 450
2021: 533
2022: 478
2023: 532
2024: 513
2025: 326
2026: 97
Papers
Preferential Temporal Difference Learning
ICML 2021
The Logical Options Framework
ICML 2021
Deciding What to Learn: A Rate-Distortion Approach
ICML 2021
Robust Reinforcement Learning using Least Squares Policy Iteration with Provable Performance Guarantees
ICML 2021
Skill Discovery for Exploration and Planning using Deep Skill Graphs
ICML 2021
Principled Exploration via Optimistic Bootstrapping and Backward Induction
ICML 2021
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
ICML 2021
TempoRL: Learning When to Act
ICML 2021
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
ICML 2021
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret
ICML 2021
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
ICML 2021
Learning Routines for Effective Off-Policy Reinforcement Learning
ICML 2021
Goal-Conditioned Reinforcement Learning with Imagined Subgoals
ICML 2021
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
ICML 2021
Improved Corruption Robust Algorithms for Episodic Reinforcement Learning
ICML 2021
Finding the Stochastic Shortest Path with Low Regret: the Adversarial Cost and Unknown Transition Case
ICML 2021
Variational Empowerment as Representation Learning for Goal-Conditioned Reinforcement Learning
ICML 2021
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
ICML 2021
Phasic Policy Gradient
ICML 2021
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
ICML 2021
Dynamic Balancing for Model Selection in Bandits and RL
ICML 2021
Offline Reinforcement Learning with Pseudometric Learning
ICML 2021
Convex Regularization in Monte-Carlo Tree Search
ICML 2021
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
ICML 2021
Kernel-Based Reinforcement Learning: A Finite-Time Analysis
ICML 2021
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