Value prediction
16 papers with code • 1 benchmarks • 0 datasets
Latest papers with no code
Towards a Better Understanding of Representation Dynamics under TD-learning
Complementary to prior work, we provide a set of analysis that sheds further light on the representation dynamics under TD-learning.
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation
We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation.
Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems
Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction.
Improving Deep Policy Gradients with Value Function Search
Deep Policy Gradient (PG) algorithms employ value networks to drive the learning of parameterized policies and reduce the variance of the gradient estimates.
Earthquake Magnitude and b value prediction model using Extreme Learning Machine
The testing RMSE came out to be around $0. 097$.
Consciousness is learning: predictive processing systems that learn by binding may perceive themselves as conscious
Machine learning algorithms have achieved superhuman performance in specific complex domains.
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency.
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective
In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent.
Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou
Customer Life Time Value (LTV) is the expected total revenue that a single user can bring to a business.
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.
