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Reinforcement Learning
2932 directly classified papers
Papers per year
2003: 1
2006: 11
2007: 18
2008: 23
2009: 14
2010: 22
2011: 24
2012: 34
2013: 26
2014: 24
2015: 14
2016: 23
2017: 79
2018: 182
2019: 255
2020: 284
2021: 333
2022: 319
2023: 315
2024: 457
2025: 419
2026: 55
Papers
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
ICML 2021
First-Order Methods for Wasserstein Distributionally Robust MDP
ICML 2021
Dynamic Balancing for Model Selection in Bandits and RL
ICML 2021
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
ICML 2021
ARMS: Antithetic-REINFORCE-Multi-Sample Gradient for Binary Variables
ICML 2021
Reinforcement Learning Under Moral Uncertainty
ICML 2021
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL
ICML 2021
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
ICML 2021
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
ICML 2021
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
ICML 2021
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
ICML 2021
Randomized Exploration in Reinforcement Learning with General Value Function Approximation
ICML 2021
Bilevel Optimization: Convergence Analysis and Enhanced Design
ICML 2021
Monotonic Robust Policy Optimization with Model Discrepancy
ICML 2021
Emphatic Algorithms for Deep Reinforcement Learning
ICML 2021
Is Pessimism Provably Efficient for Offline RL?
ICML 2021
Optimal Off-Policy Evaluation from Multiple Logging Policies
ICML 2021
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
ICML 2021
High Confidence Generalization for Reinforcement Learning
ICML 2021
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
ICML 2021
Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning
ICML 2021
Targeted Data Acquisition for Evolving Negotiation Agents
ICML 2021
Discovering symbolic policies with deep reinforcement learning
ICML 2021
Improved Regret Bound and Experience Replay in Regularized Policy Iteration
ICML 2021
OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation
ICML 2021
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