Global Certificate in RL for Decision Making
-- ViewingNowThe Global Certificate in RL for Decision Making is a comprehensive course that focuses on Reinforcement Learning (RL), a powerful AI technique for optimizing decision-making processes. This certification is crucial for professionals seeking to enhance their AI and machine learning skills, as it provides in-depth knowledge of RL concepts and their practical applications.
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• Introduction to Reinforcement Learning (RL): Understanding the basics of RL, its applications, and key concepts.
• Markov Decision Processes (MDPs): Exploring the mathematical framework behind RL, including states, actions, rewards, and transitions.
• Temporal Difference (TD) Learning: Delving into TD algorithms, such as TD(0), SARSA, and Q-learning, for estimating value functions.
• Monte Carlo (MC) Methods: Examining MC methods for estimating value functions, including MC prediction and MC control.
• Policy Gradients: Investigating policy-based methods, including the REINFORCE algorithm, for optimizing policies directly.
• Actor-Critic Methods: Combining value-function and policy-based approaches to improve both stability and sample efficiency.
• Deep Reinforcement Learning (DRL): Applying neural networks to RL for handling high-dimensional inputs, such as images and complex environments.
• Exploration vs Exploitation: Balancing the trade-off between collecting new information and maximizing rewards.
• Multi-Agent Reinforcement Learning (MARL): Extending RL concepts to multi-agent systems, addressing challenges like coordination and competition.
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