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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 301–325 of 514 papers

TitleStatusHype
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Exploratory State Representation LearningCode0
Exploring More When It Needs in Deep Reinforcement Learning—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge—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
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement LearningCode1
Learn2Hop: Learned Optimization on Rough Landscapes—0
Multimodal Reward Shaping for Efficient Exploration in Reinforcement Learning—0
MADE: Exploration via Maximizing Deviation from Explored RegionsCode1
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
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep NetworksCode1
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---Code1
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
State Entropy Maximization with Random Encoders for Efficient ExplorationCode1
Learning Memory-Dependent Continuous Control from Demonstrations—0
Meta-Thompson Sampling—0
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