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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 301–350 of 514 papers

TitleStatusHype
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
Efficient Policy Space Response Oracles—0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisCode0
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space—0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions—0
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
Map Induction: Compositional spatial submap learning for efficient exploration in novel environmentsCode0
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences—0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Efficient Exploration in Binary and Preferential Bayesian Optimization—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
Reinforcement Learning in Reward-Mixing MDPs—0
Learning to Solve Combinatorial Problems via Efficient Exploration—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations—0
Exploratory State Representation LearningCode0
Exploring More When It Needs in Deep Reinforcement Learning—0
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Bootstrapped Meta-LearningCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
Data-Efficient Exploration with Self Play for Atari—0
Impact of detecting clinical trial elements in exploration of COVID-19 literature—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Principled Exploration via Optimistic Bootstrapping and Backward InductionCode0
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores—0
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement LearningCode0
Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifoldsCode0
Efficient Optimal Selection for Composited Advertising Creatives with Tree StructureCode0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Meta-Thompson Sampling—0
Online Limited Memory Neural-Linear Bandits with Likelihood MatchingCode0
Sparse Reward Exploration via Novelty Search and EmittersCode0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
Autonomous synthesis of metastable materials—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
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