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Nov 16, 2022 · Artificial Intelligence

Fundamentals and Policy Gradient Algorithms in Reinforcement Learning with Applications to Scene Text Recognition

This article introduces the basic concepts of reinforcement learning, derives model‑based and model‑free policy gradient methods—including vanilla policy gradient and Actor‑Critic—explains their mathematical foundations, and demonstrates their use in scene text recognition and image captioning tasks.

AIactor-criticattention mechanism

0 likes · 22 min read

Fundamentals and Policy Gradient Algorithms in Reinforcement Learning with Applications to Scene Text Recognition

DaTaobao Tech

Aug 18, 2022 · Artificial Intelligence

Introduction to Deep Reinforcement Learning: Theory, Algorithms, and Applications

This article introduces deep reinforcement learning by explaining its Markov decision process foundations, then categorizes the main algorithm families—value‑based methods like DQN, policy‑based approaches such as PG/DPG/DDPG, and actor‑critic techniques including A3C, PPO, and DDPG—detailing their architectures, training procedures, and key advantages.

DQNMDPactor-critic

0 likes · 14 min read

Introduction to Deep Reinforcement Learning: Theory, Algorithms, and Applications

GuanYuan Data Tech Team

Jul 28, 2022 · Artificial Intelligence

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications

This article introduces reinforcement learning by defining agents, environments, rewards, and policies, explains key concepts such as Markov Decision Processes and Bellman equations, and surveys major algorithms—including dynamic programming, Monte‑Carlo, TD learning, policy gradients, Q‑learning, DQN, and evolution strategies—while highlighting practical challenges and notable case studies like AlphaGo Zero.

Deep LearningMDPQ-learning

0 likes · 27 min read

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications