Reinforcement Learning

About Course

This intensive course offers a comprehensive deep dive
into Reinforcement Learning (RL), an advanced AI
paradigm where autonomous agents learn optimal
sequential decision-making strategies by interacting
with dynamic, complex environments. Students will
master the core theoretical frameworks of RL, including
Markov Decision Processes, and gain extensive hands-
on experience implementing and optimizing a range of
classical and modern algorithms. Practical applications
range from training agents to master classic Atari
games to developing sophisticated control policies for
simulated robotic arms and autonomous navigation
systems.

Comprehend fundamental RL concepts, including Markov Decision Processes (MDPs), Bellman equations, and the exploration-exploitation dilemma,
along with their mathematical derivations.
Implement and debug classical RL algorithms like Q-Learning and SARSA, and modern deep RL algorithms such as Deep Q-Networks (DQN) and Policy Gradients (e.g., REINFORCE, Actor-Critic).
Design effective reward functions, robust state representations, and appropriate action spaces for diverse RL problems, optimizing for convergence and performance.
Apply advanced deep reinforcement learning techniques, including experience replay, target networks, and various policy optimization methods, to solve computationally demanding challenges.
Evaluate RL algorithm performance using metrics like cumulative reward, episodes to convergence, and policy stability, and troubleshoot common training instabilities (e.g., oscillating rewards, divergence).
Develop and deploy robust, scalable RL solutions for real-world applications in areas such as resource allocation, dynamic pricing, game AI, and intelligent
control systems.

Course Content

Foundations of Reinforcement Learning
This module provides a detailed examination of the RL framework, including agents, environments (e.g., Gridworld, CartPole), states, actions, and rewards. It features an in-depth analysis of Markov Decision Processes (MDPs), covering their formal definition, components, and fundamental properties. Students will also comprehensively explore the exploration vs. exploitation dilemma and understand key distinctions between RL, supervised, and unsupervised learning.

Dynamic Programming and Monte Carlo Methods
Students will gain a deep understanding of value functions (state-value, action-value) and policies, including the derivation and application of Bellman equations for optimal control. The module covers the implementation of policy evaluation and improvement through value iteration and policy iteration algorithms, along with Monte Carlo prediction and control methods (first-visit and every-visit MC). A comparative study of on-policy (e.g., MC Control) versus off-policy (e.g., Off-policy MC prediction) learning is also included

Temporal Difference Learning
This module provides in-depth coverage and practical implementations of TD prediction (TD(0)), SARSA (on-policy TD control), and Q-learning (off- policy TD control). Students will explore N-step TD methods and TD(λ) with eligibility traces for improved sample efficiency. The module also introduces function approximation techniques for TD learning (e.g., linear function approximation), alongside batch methods and advanced experience replay strategies.

Deep Reinforcement Learning
This section offers a rigorous introduction to Deep Q-Networks (DQN) and their significant variants (e.g., Double DQN, Dueling DQN, Prioritized Experience Replay). It includes a comprehensive study of policy gradient methods (e.g., REINFORCE) and actor-critic architectures (e.g., A2C, A3C). Advanced topics like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are covered, including their theoretical motivations and implementation challenges. Strategies for addressing common instabilities in deep RL training, such as hyperparameter tuning and credit assignment, are also discussed.

Advanced RL Techniques
This module explores multi-agent RL systems (cooperative, competitive) and hierarchical RL frameworks for complex, long-horizon tasks. Students will gain an understanding of imitation learning (e.g., Behavioral Cloning, DAGGER) and inverse RL for learning from expert demonstrations. The module also dives into model-based RL approaches (e.g., Dyna-Q, planning with learned models) and meta-learning for RL, alongside advanced strategies for effective exploration (e.g., intrinsic motivation, curiosity-driven exploration) and curriculum learning in complex environments

Applications and Implementation
This module covers practical applications of RL in diverse fields, including mastering classic Atari games (e.g., Breakout, Pong), robotic control and navigation (e.g., simulated quadruped locomotion, autonomous drone navigation), industrial process optimization (e.g., climate control in data centers, energy grid management), recommendation systems (e.g., personalized content delivery), and quantitative trading/finance (e.g., portfolio optimization). Key considerations for deploying robust, ethical, and scalable RL solutions in real-world production scenarios, including safety and interpretability, are also discussed

Capstone Project
Students will design, implement, and rigorously evaluate a complete reinforcement learning solution for a challenging, open-ended problem of their choice. This project could involve training an intelligent agent to achieve super-human performance in a complex simulation environment (e.g., OpenAI Gym's MuJoCo tasks, Unity ML-Agents), developing a dynamic policy for resource allocation in a simulated business scenario, or optimizing the control of a virtual robotic arm for a specific manipulation task. The project culminates in a comprehensive report detailing the chosen problem, algorithmic approach (including hyperparameter tuning and network architecture design), experimental setup, performance analysis using appropriate metrics, and a critical discussion of results and future work.

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This course utilizes a robust suite of resources, including industry-standard RL simulation environments (e.g., OpenAI Gym for classic control and Atari environments, MuJoCo for robotics, DeepMind Control Suite, Unity ML-Agents),
extensive practical RL algorithm implementations in Python, and leading deep learning frameworks with specialized RL capabilities (e.g., Stable Baselines3, Ray RLlib, TensorFlow Agents, PyTorch Lightning).
Students will also engage with seminal papers on state-of-the-art methods, access curated datasets for various RL tasks, and utilize advanced visualization tools (e.g., TensorBoard, custom plotting scripts) for analyzing RL agent behavior and training progress.

Reinforcement Learning

About Course

What Will You Learn?

Course Content

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Reinforcement Learning

Reinforcement Learning

About Course

What Will You Learn?

Course Content

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