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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

Showing 441450 of 514 papers

TitleStatusHype
Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates0
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement LearningCode0
NSGA-Net: Neural Architecture Search using Multi-Objective Genetic AlgorithmCode0
Thompson Sampling Algorithms for Cascading Bandits0
Exploration by Uncertainty in Reward Space0
NSGA-Net: A Multi-Objective Genetic Algorithm for Neural Architecture Search0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Directed Exploration in PAC Model-Free Reinforcement Learning0
Discovering Context Specific Causal Relationships0
Count-Based Exploration with the Successor RepresentationCode0
Show:102550
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