Authors:
(1) Jongmin Lee, Department of Mathematical Science, Seoul National University;
(2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Abstract and 1 Introduction 1.1 Notations and preliminaries 1.2 Prior works 2 Anchored Value Iteration 2.1 Accelerated rate for Bellman consistency operator 2.2 Accelerated rate for Bellman optimality opera 3 Convergence when y=1 4 Complexity lower bound 5 Approximate Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 7 Conclusion, Acknowledgments and Disclosure of Funding and References A Preliminaries B Omitted proofs in Section 2 C Omitted proofs in Section 3 D Omitted proofs in Section 4 E Omitted proofs in Section 5 F Omitted proofs in Section 6 G Broader Impacts H Limitations F Omitted proofs in Section 6 Next, we prove following key lemma Proof of Lemma 21. First, we prove first inequality in Lemma 21 by induction. If k= 0, By induction, First, we prove second inequality in Lemma 21 by induction. If k= 0, By induction. Now, we prove the first rate in Theorem 7. For the second rates of Theorem 7, we introduce following lemma. Now, we prove the second rates in Theorem 7. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Jongmin Lee, Department of Mathematical Science, Seoul National University; (2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Authors: Authors: (1) Jongmin Lee, Department of Mathematical Science, Seoul National University; (2) Ernest K. Ryu, Department of Mathematical Science, Seoul National University and Interdisciplinary Program in Artificial Intelligence, Seoul National University. Abstract and 1 Introduction Abstract and 1 Introduction 1.1 Notations and preliminaries 1.1 Notations and preliminaries 1.2 Prior works 1.2 Prior works 2 Anchored Value Iteration 2 Anchored Value Iteration 2.1 Accelerated rate for Bellman consistency operator 2.1 Accelerated rate for Bellman consistency operator 2.2 Accelerated rate for Bellman optimality opera 2.2 Accelerated rate for Bellman optimality opera 3 Convergence when y=1 3 Convergence when y=1 4 Complexity lower bound 4 Complexity lower bound 5 Approximate Anchored Value Iteration 5 Approximate Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 6 Gauss–Seidel Anchored Value Iteration 7 Conclusion, Acknowledgments and Disclosure of Funding and References 7 Conclusion, Acknowledgments and Disclosure of Funding and References A Preliminaries A Preliminaries B Omitted proofs in Section 2 B Omitted proofs in Section 2 C Omitted proofs in Section 3 C Omitted proofs in Section 3 D Omitted proofs in Section 4 D Omitted proofs in Section 4 E Omitted proofs in Section 5 E Omitted proofs in Section 5 F Omitted proofs in Section 6 F Omitted proofs in Section 6 G Broader Impacts G Broader Impacts H Limitations H Limitations F Omitted proofs in Section 6 Next, we prove following key lemma Proof of Lemma 21. First, we prove first inequality in Lemma 21 by induction. If k= 0, By induction, First, we prove second inequality in Lemma 21 by induction. If k= 0, By induction. Now, we prove the first rate in Theorem 7. For the second rates of Theorem 7, we introduce following lemma. Now, we prove the second rates in Theorem 7. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Making Sense of AI Learning Proofs

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Comprehensive Overview of Data Augmentation Methods

A Faster Path to Smarter AI: The New Anc-VI Method

Markov Decision Processes and Value Iteration in Reinforcement Learning

How Prior Studies Have Advanced Value Iteration and Acceleration in Reinforcement Learning

Anchored Value Iteration and Its Impact on Bellman Consistency in Reinforcement Learning

Anc-VI Sets New Standards in Speed for Bellman Consistency in Reinforcement Learning

A Comprehensive Overview of Data Augmentation Methods

A Faster Path to Smarter AI: The New Anc-VI Method

Markov Decision Processes and Value Iteration in Reinforcement Learning

How Prior Studies Have Advanced Value Iteration and Acceleration in Reinforcement Learning

Anchored Value Iteration and Its Impact on Bellman Consistency in Reinforcement Learning

Anc-VI Sets New Standards in Speed for Bellman Consistency in Reinforcement Learning

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps