Postgraduate Certificate in Model-Free Reinforcement Learning
-- ViewingNowModel-Free Reinforcement Learning is a rapidly growing field. This Postgraduate Certificate provides advanced training.
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- Introduction to Reinforcement Learning: Markov Decision Processes, Bellman Equations
- Model-Free RL Algorithms: Q-learning, SARSA, Deep Q-Networks (DQN)
- Deep Reinforcement Learning Architectures: Convolutional Neural Networks (CNNs) for RL, Recurrent Neural Networks (RNNs) for RL
- Advanced Model-Free Techniques: Prioritized Experience Replay, Dueling DQN, Double DQN
- Exploration-Exploitation Strategies: Epsilon-greedy, Upper Confidence Bounds (UCB), Thompson Sampling
- Function Approximation in RL: Linear Function Approximation, Deep Neural Networks
- Policy Gradient Methods: REINFORCE, Actor-Critic Methods
- Advanced Policy Gradient Algorithms: A2C, A3C, Proximal Policy Optimization (PPO)
- Applications of Model-Free RL: Robotics, Game Playing, Resource Management
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Career Role Description Reinforcement Learning Engineer (Model-Free Focus) Develops and deploys cutting-edge model-free reinforcement learning algorithms for various applications, demonstrating expertise in RL libraries and frameworks.
High industry demand.
AI Research Scientist (Model-Free RL) Conducts advanced research in model-free reinforcement learning, contributing to novel algorithms and theoretical advancements.
Focus on publication and innovation.
Machine Learning Engineer (RL Specialisation) Applies model-free reinforcement learning techniques to solve real-world problems within a broader machine learning context.
Strong problem-solving skills required.
Data Scientist (Reinforcement Learning) Leverages model-free RL to analyze complex datasets and extract actionable insights.
Requires proficiency in data manipulation and visualization.
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- ProficiencyEnglish
- ComputerInternetAccess
- BasicComputerSkills
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- ThreeFourHoursPerWeek
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