Global Certificate in Reinforcement Learning Future-Ready Techniques
-- ViewingNowThe Global Certificate in Reinforcement Learning Future-Ready Techniques is a comprehensive course that equips learners with essential skills for career advancement in the rapidly evolving field of artificial intelligence. This course emphasizes the importance of reinforcement learning, a powerful technique that enables machines to make intelligent decisions based on reward feedback.
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โข Introduction to Reinforcement Learning &mark;: Definition, History, and Core Concepts
โข Markov Decision Processes (MDPs): State, Action, Transition, and Reward
โข Q-Learning Algorithm: Temporal Difference Learning, Action-Value Function, and Optimal Policy
โข Deep Reinforcement Learning: Deep Q-Networks (DQNs), Experience Replay, and Target Network
โข Policy Gradients: REINFORCE, Actor-Critic Method, and Advantage Function
โข Deep Deterministic Policy Gradient (DDPG): Actor-Critic, Off-policy, and Continuous Action Spaces
โข Proximal Policy Optimization (PPO): Trust Region, Surrogate Objective, and Clipped Objective
โข Reinforcement Learning Applications: Game Playing, Robotics, and Recommender Systems
โข Evaluation and Interpretation: Metrics, Visualization, and Model Analysis
โข Future-Ready Techniques: Transfer Learning, Multi-Agent Reinforcement Learning, and Explainable RL
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