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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 61–70 of 514 papers

TitleStatusHype
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning—0
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
CBOL-Tuner: Classifier-pruned Bayesian optimization to explore temporally structured latent spaces for particle accelerator tuning—0
Adaptformer: Sequence models as adaptive iterative planners—0
Randomized-Grid Search for Hyperparameter Tuning in Decision Tree Model to Improve Performance of Cardiovascular Disease Classification—0
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem SolvingCode0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
Scalable Sampling for High Utility PatternsCode0
Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL—0
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