Certificate in Reinforcement Learning for AI Applications
-- ViewingNowThe Certificate in Reinforcement Learning for AI Applications is a comprehensive course designed to equip learners with essential skills in reinforcement learning, a critical area of artificial intelligence. This course covers various topics including Markov Decision Processes, Temporal Difference Learning, and Deep Reinforcement Learning.
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⢠Introduction to Reinforcement Learning – covering fundamental concepts, history, and applications of RL in AI.
⢠Markov Decision Processes (MDPs) – delving into the mathematical framework of RL, including states, actions, rewards, and transition probabilities.
⢠Temporal Difference (TD) Learning – exploring methods that learn the value function from experience, such as TD(0), SARSA, and Q-Learning.
⢠Policy Gradients – focusing on policy-based methods that optimize the policy directly, including REINFORCE, actor-critic methods, and proximal policy optimization (PPO).
⢠Deep Reinforcement Learning – discussing the integration of deep learning with RL, covering deep Q-networks (DQN), dueling DQN, and policy networks.
⢠Model-Based Reinforcement Learning – introducing methods that learn and exploit models of the environment, such as world models, dreamer, and planners.
⢠Multi-Agent Reinforcement Learning – diving into scenarios where multiple agents interact and learn in a shared environment.
⢠Exploration vs Exploitation Trade-offs – discussing strategies for balancing the need to explore new states and actions while optimizing rewards.
⢠Evaluation & Deployment of RL Systems – covering performance metrics, debugging, and deploying RL models in real-world applications.
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