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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 341–350 of 514 papers

TitleStatusHype
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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