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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 441–450 of 514 papers

TitleStatusHype
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation—0
Evolutionary Reinforcement Learning via Cooperative Coevolution—0
Explicit Planning for Efficient Exploration in Reinforcement Learning—0
Explicit Recall for Efficient Exploration—0
Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes—0
Exploration by Distributional Reinforcement Learning—0
Exploration by Learning Diverse Skills through Successor State Measures—0
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