MDP | BestHub

Alimama Tech

Sep 7, 2022 · Artificial Intelligence

Curriculum-Guided Bayesian Reinforcement Learning for ROI-Constrained Real-Time Bidding

The paper presents a Curriculum‑Guided Bayesian Reinforcement Learning (CBRL) framework that models ROI‑constrained real‑time bidding as a partially observable constrained MDP, using hard‑margin indicator rewards and a curriculum of relaxed proxy problems to achieve fast, constraint‑satisfying, Bayes‑optimal policies that outperform existing methods on large‑scale industrial data.

Bayesian RLMDPROI constraint

0 likes · 15 min read

Curriculum-Guided Bayesian Reinforcement Learning for ROI-Constrained Real-Time Bidding

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

360 Quality & Efficiency

Feb 14, 2020 · Artificial Intelligence

Applying Reinforcement Learning to UI Traversal for Automated Testing

The article explores how reinforcement learning can be used to create a test robot that performs UI traversal, discussing the challenges of full automation, defining the MDP components, feature extraction methods, reward design, and suitable RL algorithms to improve testing coverage and efficiency.

Automated TestingMDPUI traversal

0 likes · 8 min read

Applying Reinforcement Learning to UI Traversal for Automated Testing