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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 41–50 of 514 papers

TitleStatusHype
Maximum Entropy Reinforcement Learning with Diffusion PolicyCode1
Massively Scaling Explicit Policy-conditioned Value Functions—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Exploratory Diffusion Model for Unsupervised Reinforcement Learning—0
Guided Exploration for Efficient Relational Model Learning—0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation—0
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning—0
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