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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 231–240 of 514 papers

TitleStatusHype
HEAT: Hardware-Efficient Automatic Tensor Decomposition for Transformer Compression—0
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation—0
Exploring through Random Curiosity with General Value FunctionsCode0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Design of Convolutional Extreme Learning Machines for Vision-Based Navigation Around Small Bodies—0
GeoThermalCloud: Machine Learning for Geothermal Resource ExplorationCode1
Deep Active Ensemble Sampling For Image Classification—0
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
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