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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 426–450 of 514 papers

TitleStatusHype
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Efficient Exploration using Model-Based Quality-Diversity with Gradients—0
Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization—0
Efficient exploration with Double Uncertain Value Networks—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Efficient gPC-based quantification of probabilistic robustness for systems in neuroscience—0
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation—0
Efficient Policy Space Response Oracles—0
Efficient Pose and Cell Segmentation using Column Generation—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
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
End-Effect Exploration Drive for Effective Motor Learning—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
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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