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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
Adversarially Guided Actor-CriticCode1
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
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Intrinsically Guided Exploration in Meta Reinforcement Learning—0
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning—0
Online Limited Memory Neural-Linear Bandits—0
MQES: Max-Q Entropy Search for Efficient Exploration in Continuous Reinforcement Learning—0
Robotic Grasping of Fully-Occluded Objects using RF Perception—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
BeBold: Exploration Beyond the Boundary of Explored RegionsCode1
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* NeighborhoodCode1
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
Latent World Models For Intrinsically Motivated ExplorationCode1
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory—0
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