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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 351–375 of 514 papers

TitleStatusHype
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Online Limited Memory Neural-Linear Bandits—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Hierarchical reinforcement learning for efficient exploration and transfer—0
Amortized Variational Deep Q NetworkCode0
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Deep Learning based Uncertainty Decomposition for Real-time Control—0
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory—0
Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions—0
SEMI: Self-supervised Exploration via Multisensory Incongruity—0
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
End-Effect Exploration Drive for Effective Motor Learning—0
Task-agnostic Exploration in Reinforcement Learning—0
From proprioception to long-horizon planning in novel environments: A hierarchical RL model—0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Multirobot Coverage of Modular EnvironmentsCode0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
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