Executive Development Programme in Reinforcement Learning Model Development
-- ViewingNowThe Executive Development Programme in Reinforcement Learning Model Development certificate course is a comprehensive program designed to meet the growing industry demand for experts in reinforcement learning. This course emphasizes the importance of reinforcement learning, a crucial area of artificial intelligence, in creating self-learning algorithms and agents that can make decisions and take actions based on the environment to maximize cumulative reward.
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• Introduction to Reinforcement Learning – Covering the basics of reinforcement learning, its applications, and the key differences between reinforcement learning and other machine learning models. • Markov Decision Processes (MDPs) &ndsh; Diving into the mathematical framework of MDPs, including states, actions, rewards, and transition probabilities. • Q-Learning – Explaining the concept of Q-learning, its algorithm, and how it can be used to find the optimal policy in a reinforcement learning model. • Deep Q-Networks (DQNs) – Delving into the integration of deep learning and Q-learning to create DQNs, which can handle high-dimensional inputs. • Policy Gradients – Introducing policy gradients, a reinforcement learning approach that directly optimizes the policy function using gradient ascent. • Actor-Critic Methods – Covering actor-critic methods, which combine the benefits of value-based methods and policy gradients, for improved stability and sample efficiency. • Deep Deterministic Policy Gradients (DDPG) – Exploring DDPG, an algorithm that extends the actor-critic approach to continuous action spaces. • Proximal Policy Optimization (PPO) – Discussing PPO, a popular and efficient policy optimization method that strikes a balance between sample complexity and ease of implementation. • Reinforcement Learning Applications – Showcasing various real-world applications of reinforcement learning, including gaming, robotics, resource management, and personalized recommendations.
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