Reinforcement Learning Theory & Fundamentals

Explore why I don’t share full code packages and how typing out the code yourself enhances understanding. Focus on working code and practice to prepare for live coding interviews.

Explore reinforcement learning, where an agent learns from interactions with an environment to maximize rewards, and define key concepts like state, action, rewards, policy, and value function.

Explore the core components of reinforcement learning: agents, environments, states, actions, and rewards. See how their interactions drive learning and policy optimization, illustrated by a self-driving car.

Explore the history and evolution of reinforcement learning from Thorndyke and Skinner to Bellman and Markov decision processes, TD learning, Q-learning, and neural network integration.

Explore how reinforcement learning enables autonomous robotics, gaming, trading and risk management, healthcare optimization, autonomous vehicles, manufacturing efficiency, energy management, and personalized marketing.

Learn Python basics: variables, data types (int, float, str, bool, none), type conversion, dynamic typing, and core arithmetic, comparison, and logical operations with precedence rules.

Discover how Anaconda, Jupyter, and Visual Studio Code combine conda environments, pre-installed libraries like NumPy, pandas, and scikit-learn, and interactive notebooks for efficient Python development.

Explore Google Colab as a browser-based, cloud platform for writing Python, running notebooks similar to Jupyter, collaborating in real time, and leveraging seamless drive integration and preinstalled ML libraries.

Install and verify Python, Anaconda, and Visual Studio Code using the latest stable releases, add them to your path, and confirm installations via the command prompt and Anaconda Navigator.

Learn Python syntax and basic operations, including variables, data types (integers, floats, strings, booleans, none), type conversions, arithmetic, comparisons, and logical operators with precedence.

Explore core data structures in Python—lists, tuples, and sets—highlighting mutability, indexing, slicing, and common operations like append, insert, remove, and set unions, intersections, and differences.

Explore Python control structures, including if/elif/else, nesting, and logical operators, along with for and while loops, range, and loop controls like break, continue, and else.

Explore functions as reusable blocks that enable modular, maintainable code in Python, including def, lambdas, map, filter, reduce, higher-order functions, parameters and arguments, default parameters, and return values.

Explore recursion, tail recursion, factorials, currying, partial functions, closures with state, decorators, and generators to build modular, efficient and reusable code with lazy data processing.

Explore python dictionaries, a key-value data structure with unique immutable keys and flexible values, and learn access with get or in, updates, removals, iteration, and comprehensions.

Explore modules, packages, and importing libraries in Python to organize code reuse. Learn to import standard and third-party libraries like NumPy and pandas, create data frames, and handle missing data.

Master file handling in Python by opening, reading, writing, and closing files in modes; work with CSV and pandas to load, manipulate, and export data using read_csv, read_excel, and to_csv.

Learn Python exception handling to build robust code with try and except, finally, and else. Raise and use custom exceptions for input validation and meaningful user feedback.

Explore object oriented programming in Python, learning about classes, objects, attributes, and methods. Master encapsulation, inheritance, polymorphism, and the super function and multiple inheritance features.

Explore data visualization basics in Python using matplotlib and seaborn to create line plots, bar charts, histograms, heatmaps, and informative graphics.

Explore advanced list operations in Python, including list comprehensions, enumerate, and zip, with examples like squaring numbers, filtering evens, and pairing lists.

Explore the multi-armed bandit problem and balance exploration and exploitation to make smart decisions under uncertainty. See how this reinforcement learning concept applies to real-world choices.

Explore Monte Carlo methods, a randomness-based approach to solving complex problems by simulating random experiments and averaging results, used in reinforcement learning to estimate long-term rewards.

Learn how a Markov decision process models states, actions, rewards, and transitions to optimize decision making; explore value iteration, policy iteration, and practical applications in robotics and autonomous vehicles.

Explore how a dynamic pricing MDP models demand states, actions, and transition probabilities to maximize discounted revenue. Identify the policy and objective that maximize expected revenue over the planning horizon.

Explore a practical MDP Python example that defines states, actions, transitions, and rewards, then uses dynamic programming with backward induction to derive the optimal policy and value function.

Analyze a reinforcement learning MDP output that maps time periods and demand states to optimal price levels, revealing low-price strategies and fluctuating pricing to maximize revenue.

Discover dynamic programming as a recursive optimization technique that decomposes problems into subproblems, stores solutions, and uses the optimality principle and Bellman equation.

Formulate a multi-dimensional knapsack model to maximize value while respecting weight, volume, and other truck constraints. Use dynamic programming to obtain optimal, real-time loading decisions for the fleet.

Explore mathematical modeling for the knapsack problem by formulating sets, indices, weight and volume constraints, and binary decision variables to maximize value under capacity limits.

Implement a Python dynamic programming solution for the multidimensional knapsack, maximizing value under per-dimension capacities. Track items, weights across dimensions, and a dp table to update optimal combinations safely.

Examine dynamic programming for the multidimensional knapsack problem, producing a maximum value of 50 by evaluating item combinations within weight and volume constraints.

Explore reinforcement learning with a Python implementation of an actor-critic network using reinforce with baseline, combining policy and value heads, entropy regularization, and training in OpenAI Gym environments.

Explore generalized advantage estimation (GAE) and how lambda balances bias and variance for stable policy updates, with trajectory collection, TD errors, and value-based learning.

Learn how generalized advantage estimation (gae) is implemented to reduce variance in policy gradient methods like PPO by computing advantages and returns from rewards, values, and dones, with normalization.

Master ME-TRPO by using a model ensemble to simulate dynamics and train via virtual rollouts within a trust region and KL divergence constraint, improving sample efficiency and robustness.

Discover the Impala architecture, an importance weighted actor-learner framework that decouples actors from a central learner, enabling parallel data collection, v-trace corrections, and scalable, asynchronous reinforcement learning.