Efficient Exploration
144 papers with code • 0 benchmarks • 2 datasets
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
Benchmarks
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Libraries
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Replica
House3D Environment
Latest papers
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data.
Consensus-based construction of high-dimensional free energy surface
Lyuliyao/consensus-sampling-method-for-expolering-high-dimensional-energy-surface
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One essential problem in quantifying the collective behaviors of molecular systems lies in the accurate construction of free energy surfaces (FESs).
PGDQN: Preference-Guided Deep Q-Network
OscarHuangWind/Preference-Guided-DQN-Atari
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IEEE Transactions on Neural Networks and Learning Systems 2023
Stochastic exploration is the key to the success of the Deep Q-network (DQN) algorithm.
Feature Interaction Aware Automated Data Representation Transformation
Creating an effective representation space is crucial for mitigating the curse of dimensionality, enhancing model generalization, addressing data sparsity, and leveraging classical models more effectively.
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug Design
amine179/curiosity-rl-for-drug-design
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In this work, we introduce a curiosity-driven method to force the model to navigate many parts of the chemical space, therefore, achieving higher desirability and diversity as well.
Go Beyond Imagination: Maximizing Episodic Reachability with World Models
Efficient exploration is a challenging topic in reinforcement learning, especially for sparse reward tasks.
Improving Protein Optimization with Smoothed Fitness Landscapes
kirjner/ggs
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The ability to engineer novel proteins with higher fitness for a desired property would be revolutionary for biotechnology and medicine.
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms.
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization
Bayesian optimization (BO) provides a powerful framework for optimizing black-box, expensive-to-evaluate functions.
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
We address three critical questions in this emerging research field: i) the importance and urgency of label-efficient learning in point cloud processing, ii) the subfields it encompasses, and iii) the progress achieved in this area.
