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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 276–300 of 514 papers

TitleStatusHype
Sample Efficient Robot Learning in Supervised Effect Prediction Tasks—0
Sampling for Model Predictive Trajectory Planning in Autonomous Driving using Normalizing Flows—0
SAR Image Despeckling Based on Convolutional Denoising Autoencoder—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scaling active inference—0
Scattered Forest Search: Smarter Code Space Exploration with LLMs—0
Scheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
SEMI: Self-supervised Exploration via Multisensory Incongruity—0
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models—0
SHIRO: Soft Hierarchical Reinforcement Learning—0
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks—0
Solving Diffusion ODEs with Optimal Boundary Conditions for Better Image Super-Resolution—0
Sparse graphs using exchangeable random measures—0
Misspecification-robust likelihood-free inference in high dimensions—0
n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank—0
Structured exploration in the finite horizon linear quadratic dual control problem—0
Successor-Predecessor Intrinsic Exploration—0
Synergistic Fusion of Multi-Source Knowledge via Evidence Theory for High-Entropy Alloy Discovery—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
Task-agnostic Exploration in Reinforcement Learning—0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
The Eigenoption-Critic Framework—0
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors—0
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