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This is a repository to maintain all codes for homework assignments and Projects done during this course.\n\n## Week 1 - Markov Decision Processes\n1. The Bellman Equation\n2. * Markov Decision Process *\n3. Value Functions\n4. Homework - OpenAI Gym Installation and Basics\n5. Sensor Networks\n6. Google Dopamine\n\n## Week 2 - Dynamic Programming\n1. Sports Betting\n2. * Bellman Advanced *\n3. Dynamic Programming tutorial\n4. Dynamic Programming Reading Assignments\n5. * Value and Policy Iterations *\n6. Homework - Frozen Lake Problem with Value and Policy Iterations\n7. IPhoneX supply chain\n\n## Week 3 - Monte Carlo Methods\n1. Internet of Things Optmisation\n2. Exploration vs Exploitation\n3. Exploration vs Exploitation (Multi Arm Bandits)\n4. Monte Carlo Coding Tutorial\n5. * MC Control and MC Prediction *\n6. Monte Carlo Methods\n7. Q Learning for trading\n8. Homework Assignment - Monte Carlo\n9. Tensor Processing Units\n\n## Week 4 - Model Free Learning\n1. Dopamine in Neuroscience\n2. * Reading Assignments - Model Based vs Model Free Learning *\n3. Homework Assignment (Q Learning)\n4. * Temporal Difference Learning *\n5. Q Learning for Ride Sharing\n6. Quantum Interview\n\n## Week 5 - RL in Continuous Spaces\n* Skipped *\n\n## Week 6 - Deep Reinforcement Learning\n1. Deep RL for Database Optimization\n2. Deep Q Learning Pong Tutorial\n3. * Prioritized Experience Replay (PER) *\n4. Dueling DQN\n5. Neural Networks Study Guide\n6. Neural Networks Quiz\n7. * Reading Assignment (DQN Improvements) *\n8. Homework Assignment (Deep Q Learning)\n\n## Week 7 - Policy Based Methods\n1. * Neuroevolution Meta-Learning *\n2. Policy Search Algorithms\n3. * Evolutionary Algorithms Study Guide *\n4. Homework Assignment (Neuroevolution)\n5. Control Theory\n\n## Week 8 - Policy Gradient Methods\n1. Policy Gradients Math Primer\n2. Policy Gradient Methods Tutorial\n3. Policy Gradient methods (REINFORCE)\n4. Evolved Policy Gradients\n5. * Policy Gradients Study Guide *\n6. Homework Assignment (Monte Carlo Policy Gradients)\n7. Artificial Curiosity\n\n## Week 9 - Actor Critic Methods\n1. Drone Flight Controller\n2. Asynchronous Advantage Actor Critic (A3C) Tutorial\n3. * Reading Assignment (Actor Critic Algorithms) *\n4. Homework Assignment (A2C)\n5. Continuous Action Space Actor Critic Tutorial\n6. * Master Roboschool with PPO (Coding Tutorial) *\n7. * PPO (Proximal Policy Optimization) *\n8. Bayesian Actor Critic\n9. Actor Critic Methods Study Guide\n\n## Week 10 - Multi Agent RL\n1. Move37\n2. Reading Assignment (Cooperative Agents)\n3. Inverse Reinforcement Learning\n4. MARL – Multi Agent Reinforcement Learning\n5. Multi Agent and Inverse RL Study Guide\n6. AlphaGo Zero Tutorial Part 3 – Neural Network Architecture\n7. 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