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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 171–180 of 514 papers

TitleStatusHype
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Embodied Agents for Efficient Exploration and Smart Scene Description—0
Emotion-Agent: Unsupervised Deep Reinforcement Learning with Distribution-Prototype Reward for Continuous Emotional EEG Analysis—0
Efficient exploration of zero-sum stochastic games—0
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
Explicit Recall for Efficient Exploration—0
Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization—0
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