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Exploration

86 directly classified papers

Papers per year

Papers

Near-Optimal Randomized Exploration for Tabular Markov Decision Processes NIPS 2022
Optimistic Initialization for Exploration in Continuous Control AAAI 2022
A Mixture Of Surprises for Unsupervised Reinforcement Learning NIPS 2022
Offline Reinforcement Learning as Anti-exploration AAAI 2022
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games ACL 2022
Exploration via Elliptical Episodic Bonuses NIPS 2022
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity NIPS 2022
Exploring through Random Curiosity with General Value Functions NIPS 2022
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL NIPS 2022
An Analysis of Ensemble Sampling NIPS 2022
Diversified Recommendations for Agents with Adaptive Preferences NIPS 2022
Exploration via Planning for Information about the Optimal Trajectory NIPS 2022
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward NIPS 2022
Metrics and Continuity in Reinforcement Learning AAAI 2021
Exploration via State influence Modeling AAAI 2021
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration AAAI 2021
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks NIPS 2021
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration NIPS 2021
Local policy search with Bayesian optimization NIPS 2021
NovelD: A Simple yet Effective Exploration Criterion NIPS 2021
Computing an Efficient Exploration Basis for Learning with Univariate Polynomial Features AAAI 2021
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning IJCAI 2021
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning NIPS 2021
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback AAAI 2021
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game ICML 2021