- Neural Temporal|Difference Learning Converges to Global Optima🔍
- Neural Temporal|Difference and Q|Learning Provably Converge to ...🔍
- Neural temporal|difference learning converges to global optima🔍
- Neural Temporal Difference and Q Learning Provably Converge to ...🔍
- [1905.10027] Neural Temporal|Difference Learning Converges to ...🔍
- NEURAL POLICY GRADIENT METHODS🔍
- Convergent Temporal|Difference Learning with Arbitrary Smooth ...🔍
- Publications🔍
Neural Temporal|Difference Learning Converges to Global Optima
Neural Temporal-Difference Learning Converges to Global Optima
In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for ...
Neural Temporal-Difference Learning Converges to Global Optima
Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However, due.
Neural Temporal-Difference and Q-Learning Provably Converge to ...
Title:Neural Temporal-Difference and Q-Learning Provably Converge to Global Optima ; Subjects: Machine Learning (cs.LG); Artificial Intelligence ...
Neural Temporal-Difference Learning Converges to Global Optima
In particular, we show how such global convergence is enabled by the overparametrization of neural networks, which also plays a vital role in the empirical ...
Neural temporal-difference learning converges to global optima
Abstract. Temporal-difference learning (TD), coupled with neural networks, is among the most fundamental building blocks of deep reinforcement learning. However ...
Neural Temporal-Difference Learning Converges to Global Optima
For such nonlinear function approximation, TD algorithm may not even converge. The paper exploits the fact that the overparameterized neural network has ...
Neural Temporal Difference and Q Learning Provably Converge to ...
In this paper, we prove for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected ...
Neural Temporal-Difference and Q-Learning Provably Converge to ...
forcement learning than nonlinear TD, provably converges to the global optimum of MSPBE. There exist various extensions of TD, including ...
Neural Temporal Difference and Q Learning Provably Converge to ...
As a result, the global convergence of neural TD remains unclear. In this paper, we prove for the first time that neural TD converges at a sublinear rate to the ...
Neural Temporal Difference and Q Learning Provably Converge to ...
This paper proves for the first time that neural TD converges at a sublinear rate to the global optimum of the mean-squared projected Bellman error for ...
Neural Temporal-Difference Learning Converges to Global Optima
Request PDF | Neural Temporal-Difference Learning Converges to Global Optima | Temporal-difference learning (TD), coupled with neural networks, ...
Neural Temporal Difference and Q Learning Provably Converge to ...
Request PDF | Neural Temporal Difference and Q Learning Provably Converge to Global Optima | Temporal difference learning (TD), coupled with ...
[1905.10027] Neural Temporal-Difference Learning Converges to ...
[1905.10027] Neural Temporal-Difference Learning Converges to Global Optima ... Does this mean that we can use deep neural networks in TD(0) ...
NEURAL POLICY GRADIENT METHODS: GLOBAL OPTIMALITY ...
Neural temporal-difference learning converges to global optima. arXiv preprint arXiv:1905.10027. Cao, Y. and Gu, Q. (2019a). Generalization bounds of ...
Convergent Temporal-Difference Learning with Arbitrary Smooth ...
... global optima of the proposed objective function will not modify the set of solutions that the usual TD(0) algorithm would find (if it would indeed converge).
Convergent Temporal-Difference Learning with Arbitrary Smooth ...
... global optima of the proposed objective function will not modify the set of solutions that the usual TD(0) algorithm would find (if it would indeed converge).
NeurIPS 2019. Neural Temporal-Difference Learning Converges to Global Optima Qi Cai, Zhuoran Yang, Jason D. Lee, and Zhaoran Wang. NeurIPS 2019. Incremental ...
Neural Policy Gradient Methods: Global Optimality and Rates of...
We answer both the questions affirmatively in the overparameterized regime. In detail, we prove that neural natural policy gradient converges to ...
Temporal-difference learning with nonlinear function approximation
Neural temporal-difference learning converges to global optima, 2019. preprint, arXiv:1905.10027. Lénaıc Chizat and Francis Bach. On the global convergence ...
Why should weights of Neural Networks be initialized to random ...
The basic training algorithms are greedy in nature - they do not find the global optimum, but rather - "nearest" local solution. As the result, ...