- What is meant by a local optima problem in neural networks?🔍
- Difference between Evolutionary Strategies and Reinforcement ...🔍
- A Convergent O🔍
- Why is Newton's method not widely used in machine learning?🔍
- Sample Complexity and Overparameterization Bounds for Temporal ...🔍
- How to overcome a local minimum problem in neural networks🔍
- Choose function for On|Policy prediction with approximation🔍
- Provably Efficient Reinforcement Learning🔍
Neural Temporal|Difference Learning Converges to Global Optima
What is meant by a local optima problem in neural networks? - Quora
I'd say that a local optimum is a solution that cannot be improved upon by making a small change to it. So the global optimum is also a local ...
Difference between Evolutionary Strategies and Reinforcement ...
... Difference learning (TD) and per ... So RL can converge to a local optima converging faster while ES converges slower to a global minima.
A Convergent O(n) Algorithm for Off-policy Temporal-difference ...
We introduce the first temporal-difference learning algorithm that is stable with linear function approximation and off-policy training, for any finite ...
Why is Newton's method not widely used in machine learning?
Using linesearch with the Wolfe conditions or using or trust regions prevents convergence to saddle points. A proper gradient descent ...
Sample Complexity and Overparameterization Bounds for Temporal ...
Neural temporal-difference learning converges to global optima. In Advances ... Toward moderate overparam- eterization: Global convergence guarantees for training ...
How to overcome a local minimum problem in neural networks - Quora
First thing that comes to mind is the learning rate. If the learning rate is too small then it will take forever to converge (since a linearly ...
Choose function for On-Policy prediction with approximation
The difference being that the neural network can either take as input ... Q-Learning convergence to optimal policy · 3 · How do I combine ...
Provably Efficient Reinforcement Learning - ProQuest
The first part Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima studies how gradient-based algorithm makes reinforcement learning ...
Why doesn't Q-learning converge when using function approximation?
Consequently, even if you chose a good learning rate, and visit all states infinitely often, your approximation function will never converge to ...
Zhuoran Yang - Google Scholar
2023. Neural temporal difference and q learning provably converge to global optima. Q Cai, Z Yang, JD Lee, Z Wang. Mathematics of Operations Research 49 (1) ...
Stability and Generalization of Learning Algorithms that Converge to ...
local/global minimizers in linear neural networks and refor- mulate ... Stability and Generalization of Learning Algorithms that Converge to Global Optima.
Neu- ral temporal-difference learning converges to global op- tima. In Advances in Neural Information Processing Sys- tems (NeurIPS), 11312–11322. Castillo ...
Convergence of reinforcement learning with general function ...
Although the standard temporal-difference learning algorithm has been shown to ... mate global optimum for any agnostically learnable hypothesis class ...
Finite-Time Analysis for Double Q-learning - NIPS papers
converges determines the overall convergence rate of double Q-learning. ... Neural temporal-difference learning converges to global optima. In Advances ...
Generative Adversarial Imitation Learning with Neural Network ...
ear convergence to the globally optimal solution. To the best of our ... Neural temporal- difference learning converges to global optima. arXiv.
Neural temporal-difference learning converges to global optima. Q Cai, Z Yang, JD Lee, Z Wang. Advances in Neural Information Processing Systems 32, 2019. 149* ...
Comprehensive overview of solvers/optimizers in Deep Learning
... optima, and gradient descent may not converge to the global optimum. ... Optimizers adjust the weights of the neural network during training ...
Value Function Approximation - Lecture Notes for Deep Learning ...
• Linear TD(0) converges (close) to global optimum. Deep Learning and ... • LSTD – Least Squares Temporal Difference Learning. UU𝜋𝜋 sstt ...
Reinforcement learning - Wikipedia
Many gradient-free methods can achieve (in theory and in the limit) a global optimum. ... Temporal-difference-based algorithms converge under a wider set of ...
Effect of batch size on training dynamics | by Kevin Shen | Mini Distill
... converge to the global optima. It will bounce around the global optima, staying outside some ϵ-ball of the optima where ϵ depends on the ...