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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 326–350 of 514 papers

TitleStatusHype
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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