Artificial Intelligence: Reinforcement Learning in Python

Explore the explore-exploit dilemma with a two-armed bandit analogy and learn four adaptive algorithms—epsilon greedy, optimistic initial value method, UCB one, and Thompson sampling.

Apply the explore-exploit dilemma to real-world advertising and web design by comparing options with experiments, using Gaussian distributions to measure click-through and conversion rates.

Learn how epsilon-greedy balances exploration and exploitation in reinforcement learning by occasionally choosing a random bandit, updating mean rewards, and adapting epsilon over time.

Learn to calculate the sample mean for binary 0/1 rewards, show it is the maximum likelihood estimate of the Bernoulli parameter, and enable constant time and space updates for epsilon-greedy.

Begin with a hands-on epsilon-greedy exercise in reinforcement learning, building a bandit simulator from scratch in Python, and learn exploration versus exploitation through a complete loop and updates.

Explore bandit methods with a hands-on approach to explore-exploit in reinforcement learning, designing a slot-machine pull function and tracking average rewards.

Implement an epsilon-greedy bandit in Python to balance exploration and exploitation with simulated trials. Analyze estimated versus true win rates and extend with decaying epsilon strategies.

Compare epsilon-greedy performance with Gaussian rewards across three arms, revealing how different epsilons influence convergence, cumulative reward, and exploration-exploitation tradeoffs.

Explore the optimistic initial values method for the explore-exploit dilemma, using large initial estimates with greedy selection to induce exploration and discuss initialization as a hyperparameter.

Study optimistic initial values in a beginner’s exercise for multi-armed bandits, covering the bandit class setup, p estimates, and selecting the next arm, with a full exercise for advanced learners.

Explore the optimistic initial values method for bandits, including initializing P estimates and n_A, greedy selection, and updates, and compare its performance to epsilon-greedy.

Learn how the UCB1 algorithm uses the sample mean plus an upper confidence bound to balance exploration and exploitation in reinforcement learning.

Learn to implement the UCB1 algorithm in a beginner-friendly exercise, including bandit setup, experiment loop, and plotting results, with a focus on applying the UCB formula.

Implement the UCB1 algorithm in Python, detailing the UCB function formula, initialization and main loops, and show convergence to the optimal band with accurate mean estimates and cumulative reward.

Explore Bayesian bandits and Thompson sampling with a beta prior for the Bernoulli mean theta, updating to a beta posterior as data arrives, illustrating conjugacy and online learning.

Explore Thompson sampling, drawing from the posterior distribution to select arms instead of using an upper bound. Update beliefs with data, visualize beta posteriors, and compare exploration-exploitation in Bayesian bandits.

Explore implementing the Thompson sampling algorithm in code with a beginner-friendly fill-in-the-blanks exercise. Learn to use the beta distribution for updating bandit posteriors in a Python reinforcement learning context.

Implements Thompson sampling for a Bayesian bandit by initializing beta priors, sampling from posteriors, and updating parameters, showing convergence to the optimal bandit and fat posteriors for exploration.

Extend Thompson sampling to gaussian rewards with a gaussian likelihood and known precision. Derive the posterior parameters for the mean under conjugate priors and implement single-sample updates.

Explore implementing the Bayesian bandit for normally distributed rewards using gaussian posteriors and Thompson sampling, with code that initializes and updates the gaussian parameters.

Explore the bayesian bandit with gaussian rewards, adjust it for true means 5, 10, and 20, and diagnose why simple replacements break convergence.

Libraries can't replace the essential math and coding skills; real-world practice hinges on backend engineering and data infrastructure, databases, batch jobs, and cross-language systems.

Explore nonstationary bandits by updating the mean with a constant learning rate via an exponentially weighted moving average, enabling the model to adapt as rewards change over time.

Examine the explore-exploit dilemma with epsilon-greedy, upper confidence bound, optimistic initial values, and Bayesian bandit approaches, and why online learning with real data testing remains impractical for true click-through rates.

Explore why there is no single correct bandit design and how naming reflects mathematical concepts. Learn how many-to-many modeling, environment and agent roles, and real-world versus simulation considerations guide implementation.

Explore a practical suggestion box for reinforcement learning students, collecting background, course difficulty, missed explanations, and future topic requests to tailor and improve the Python-based course.

Explore how reinforcement learning differs from supervised and unsupervised learning, and see how agents interact with dynamic environments, from vacuuming a house to playing games and beyond.

Explore the fundamentals of Markov decision processes in reinforcement learning. Understand the return, value function, and Bellman equation, with grid world visuals and the roots of Q-learning.

Explore grid world as a simple reinforcement learning environment, defining episodes, terminal states, state and action spaces, and learning policies—deterministic, probabilistic, and epsilon-greedy.

Design the reward structure to guide the agent toward the goal in grid worlds and mazes, using plus one for success and minus one to discourage wandering.

Learn how the Markov property limits dependencies to the previous state, explore the state transition matrix, and see language modeling and deep Q learning illustrate first-order assumptions in practice.

Explore the Markov decision process as a discrete-time stochastic control framework with an agent and environment, where actions determine next states and rewards through p(s'|s,a) and a policy.

Maximize the return by summing future rewards—actions affect future, not past—using gamma discount to emphasize near-term rewards and improve reinforcement learning training.

Define the value function as the expected return from a state under a policy. Show how state, policy, and dynamics affect future rewards, with terminal states valued at zero.

View the Bellman equation as a recursive, one-step lookahead for the value function, using the law of total expectation to compute expected returns from next states, underpinning Q-learning.

Explore the Bellman equation by expanding its terms over state, action, reward, and next state, highlighting policy and environment dynamics and the move toward scalable solutions.

Examine the Bellman equation for value functions, detailing state value V and action value Q, and how policies, including stochastic ones, shape future rewards.

Learn how to solve the Bellman equation for the value function using simple two- and three-state examples, including deterministic and random transitions, terminal states, rewards, and a gamma of 0.9.

Explore how to find the best policy and the best value function using the Bellman optimal equation, linking v* and Q* with policy evaluation.

Explains deriving the optimal policy from V star or Q star using arg max and contrasts evaluation versus control, outlining dynamic programming, Monte Carlo, and temporal difference methods.

Explore the foundations of Markov decision processes with a grid world, defining states, actions, rewards, policies, and values, and derive the Bellman equation to maximize the expected return.

Introduces how to use the Markov decision process to solve real reinforcement learning problems, outlining prediction and control tasks and applying the Bellman equation through policy iteration and value iteration.

Explore iterative policy evaluation, a dynamic programming approach using the Bellman equation to compute the state-value function V^π for a policy, converging via Delta to a fixed point.

Design reinforcement learning programs with a consistent interface for prediction and control problems. Initialize value functions, run episodes, collect states, actions, rewards, and iteratively update policies and values.

Implement a grid world environment in code, enabling policy evaluation and policy iteration, by defining state management, moves, terminal states, rewards, and actions.

Explore iterative policy evaluation in a deterministic grid world by building transition probabilities, a policy dictionary, and value updates using Bellman equations, with visualization helpers and convergence checks.

Explore windy grid world with probabilistic transitions stored in a dictionary of state-action to next-state probabilities. Adopt test-driven development to implement the move function and deterministic rewards.

Implement iterative policy evaluation in Windy Grid World with probabilistic state transitions and a probabilistic policy, building transition and rewards dictionaries and applying the Bellman equation to observe convergence.

Explore policy improvement in reinforcement learning: compare Q and V values, choose Amax actions, update policies across states, and approach the Bellman optimality equation for the optimal policy.

Policy iteration refines a policy by alternating evaluation and improvement until stability. It uses value functions and Bellman updates to guide each step.

Develop and implement policy iteration in a deterministic grid world, performing policy evaluation and improvement, using transition probabilities, rewards, and the Bellman optimality equation to converge on an optimal policy.

Value iteration speeds policy optimization by updating value function with the Bellman max, skipping policy evaluation, and converging when delta falls below a threshold toward the optimal value and policy.

Implement value iteration in a grid world by computing transition probabilities and rewards, applying Bellman optimality, and extracting the optimal value function and policy.

Review dynamic programming in reinforcement learning, covering policy evaluation, policy improvement, and value and disvalue iteration; contrast model-based and model-free approaches and learning from experience.

Explore Monte Carlo methods to estimate expected values from samples. Apply sample means to approximate value functions under a policy in reinforcement learning, addressing environment dynamics and the explore-exploit dilemma.

Use Monte Carlo policy evaluation to estimate state values by averaging returns from episodes generated under a given policy, including first-visit and every-visit variants.

Implement Monte Carlo policy evaluation in a grid world to predict value functions, using first-visit Monte Carlo, episode sampling, and a 0.9 discount, with random starts and terminal states.

Discover how Monte Carlo control solves the control problem by using policy evaluation and improvement to converge toward the optimal policy.

Implement Monte Carlo control in code by learning a policy over state and action pairs through first-visit updates, updating values and policy on a grid world.

discover Monte Carlo control without exploring starts by using epsilon-greedy policies, first-visit updates, and Q estimates to learn the optimal state-action choices without impractical exploration.

Monte Carlo control with an epsilon-greedy policy without exploring starts in a grid world, updating Q via sample means and tracking state and state-action counts.

Apply Monte Carlo methods to reinforcement learning by estimating values from sample means as agents learn from experience. Highlight policy iteration in sample-based learning and epsilon-greedy exploration for Q-based control.

Learn temporal difference learning, a bootstrapping, sample-based method that blends dynamic programming and Monte Carlo ideas, and explore two variants, Sauza and Q-learning, for prediction and control.

Explore TD(0) prediction and how bootstrapping updates the value function with the one-step target r plus gamma V(s'), avoiding full-episode reliance like Monte Carlo.

Demonstrate implementing TD(0) prediction in Python code using a grid environment, with epsilon-greedy action selection, a constant learning rate alpha, and updating the value function and deltas.

Apply temporal-difference learning to control with the SARSA algorithm, updating Q values from state, action, reward, next state, and next action in an epsilon-greedy on-policy framework.

Implement a SARSA-based control loop in Python using epsilon-greedy action selection from q, on a grid world with -0.1 step cost, and derive the optimal policy and value function.

explore q-learning, which updates the q-function using the max future q value, making it off-policy with a greedy target policy and an epsilon-greedy behavior policy.

Implement q-learning in Python by building a q-table, applying epsilon-greedy actions, updating values with amax and max, and deriving the final policy for a grid environment.

Convert Monte Carlo into a one-step update via the Bellman equation, bootstrap value estimates with temporal difference learning, and compare on-policy methods like Sarsa with off-policy Q-learning.

Explore function approximation in reinforcement learning, moving beyond tabular methods with linear models, linear regression, stochastic gradient descent, and feature engineering for prediction and control, applied to grid world.

Explore linear regression as a foundation for prediction and control in reinforcement learning, using function approximation with W^T X and squared error minimized by gradient descent and stochastic gradient descent.

Apply feature engineering to transform inputs with basis expansions, such as polynomials and radial basis functions, enabling linear models to approximate nonlinear value functions in reinforcement learning.

Use function approximation to solve the prediction task with a feature vector X and weight vector W, comparing Monte Carlo prediction and TD learning with gradient descent on squared error.

Implement function approximation for prediction with temporal difference learning using an rbf feature expansion and epsilon-greedy control, updating weights via td error and tracking mean squared error per episode.

Apply function approximation for control in reinforcement learning by encoding state-action pairs with one-hot actions, using feature expansion such as RBF kernel, and updating Q-values via gradient descent in Q-learning.

Learn function approximation for control with q-learning, using action-to-integer mappings, one-hot encoding, and greedy action selection to derive value and policy.

Explore CartPole in OpenAI Gym with q learning and function approximation. Grasp the four-state vector, left/right actions, and +1 rewards until termination at 200 steps, plus a random policy example.

Build an optimal CartPole controller in a gym environment with epsilon-greedy learning and gamma tuning. Train with a reusable model class and observe an average reward per episode of 200.

Practice exercises reinforce function approximation in reinforcement learning: test feature expansions, target value forms, batch gradient descent, neural networks with deep libraries, optimization techniques, and state-action representations for Q-learning.

Learn function approximation for reinforcement learning with linear models, stochastic gradient descent, and feature engineering, polynomial expansions, and RBF kernel to predict values and guide control.

This course teaches standard reinforcement learning topics—explore-exploit, dynamic programming, Monte Carlo, and Q learning—while providing complete code examples and real-world applications like stock trading.

Begin with the course prerequisites and complete prior exercises to assess readiness for this reinforcement learning section; avoid skipping ahead.

Explore reinforcement learning in stock trading as an agent that buys and sells in a market environment, guided by states and rewards.

An open gym-style stock trading environment that simulates three stocks: Apple, Motorola, and Starbucks, with state as shares, prices, and cash; actions buy/sell/hold; reward equals portfolio value change.

Describe a state-based linear regression Q-learning model with one output per action, updating only the chosen action using gradient descent with momentum in a deep reinforcement learning setting.

Designs a reinforcement learning trading program with train and test modes, using q-learning and epsilon-greedy updates to train on past data and test on future prices, tracking portfolio value.

Explore reinforcement learning for stock trading with a linear regression model trained via stochastic gradient descent, using momentum, mean squared error, and a single step gradient update.

Define a multi-stock reinforcement learning environment with state as shares, prices, and cash, and a 27-action space for buy, hold, or sell across three stocks, plus reset and step dynamics.

Learn how an agent uses epsilon-greedy action selection, gamma discounting, and a neural model to learn from state, action, reward, next state, maximize future rewards, and save and load weights.

Finish the reinforcement learning trading script by running episodes, updating the environment, and training or testing the agent to optimize the portfolio value on five years of time series data.

Discusses evaluating a stock trading reinforcement learning agent in Python, comparing its performance to a random agent, and exploring hyperparameters, data sets, and potential extensions like metadata and returns.