Exploration

Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback AAAI 2021 Computing an Efficient Exploration Basis for Learning with Univariate Polynomial Features AAAI 2021 Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks NeurIPS 2021 Exploration via State influence Modeling AAAI 2021 Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration AAAI 2021 Interesting Object, Curious Agent: Learning Task-Agnostic Exploration NeurIPS 2021 Local policy search with Bayesian optimization NeurIPS 2021 Metrics and Continuity in Reinforcement Learning AAAI 2021 Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning IJCAI 2021 NovelD: A Simple yet Effective Exploration Criterion NeurIPS 2021 On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game ICML 2021 Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments ICLR 2021 Sequential Generative Exploration Model for Partially Observable Reinforcement Learning AAAI 2021 Temporally-Extended ε-Greedy Exploration ICLR 2021 There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning NeurIPS 2021 Active Vision for Early Recognition of Human Actions CVPR 2020 Adaptive Probing Policies for Shortest Path Routing NeurIPS 2020 Beliefs We Can Believe in: Replacing Assumptions with Data in Real-Time Search AAAI 2020 Novel Is Not Always Better: On the Relation between Novelty and Dominance Pruning AAAI 2020 Reinforcement Learning with Non-Markovian Rewards AAAI 2020 Reward-Free Exploration for Reinforcement Learning ICML 2020 Better Exploration with Optimistic Actor Critic NeurIPS 2019 Dead-ends and Secure Exploration in Reinforcement Learning ICML 2019 Diverse Exploration via Conjugate Policies for Policy Gradient Methods AAAI 2019 Efficient Pure Exploration in Adaptive Round model NeurIPS 2019