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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 291–300 of 514 papers

TitleStatusHype
n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank—0
Structured exploration in the finite horizon linear quadratic dual control problem—0
Successor-Predecessor Intrinsic Exploration—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
Task-agnostic Exploration in Reinforcement Learning—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
The Eigenoption-Critic Framework—0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
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