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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 321–330 of 514 papers

TitleStatusHype
Reinforcement Learning in Reward-Mixing MDPs—0
Learning to Solve Combinatorial Problems via Efficient Exploration—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations—0
Exploratory State Representation LearningCode0
Exploring More When It Needs in Deep Reinforcement Learning—0
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge—0
Differentially Evolving Memory Ensembles: Pareto Optimization based on Computational Intelligence for Embedded Memories on a System Level—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Bootstrapped Meta-LearningCode0
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