Outline augmented random search fundamentals, the perceptron, and the iris algorithm. Explain how rewards are maximized through learning from interactions and compare ARS to iris using finite differences.

Discover augmented random search (ARS) and how it trains magico figures in the Mejico engine through trial and error, using environmental feedback to learn control of multiple degrees of freedom.

Explore how a perceptron processes inputs by weighted sums to produce outputs, using a weights matrix and simple matrix multiplication, highlighting shallow learning and single-layer intuition.

Explore how a perceptron with three inputs and two outputs learns from an environment by adjusting weights based on rewards received during episodes, building intuition with a toy walking agent.

discover how the method of finite differences updates weights by evaluating positive and negative perturbations under the random search method, using episode rewards to guide learning.

Explore augmented random search, focusing on scaling updates by reward standard deviation, online state normalization, and discarding low-reward directions to improve learning.

Compare evolution strategies ARS with other ai algorithms, emphasizing policy-space exploration, finite-differences updates, and shallow versus deep learning, while noting ARS delivers faster, higher rewards in specific applications.

Start this practical tutorial to build the augmented random search AI, a simple yet powerful model that surpasses AI algorithms we've worked on, using Anaconda, Python 3.6, and Spider IDE.

Explains setting up a reinforcement learning environment with phablet and cheetah, mapping a research paper into code using a randomized search with v2 normalization to optimize a policy.

This lecture walks through building a simple one-layer ai with numpy, defines fixed hyperparameters in a class, and sets up the environment, learning rate, and directions for augmented random search.

Build a state normalization class that online learns the mean and variance of input vectors and normalizes them by subtracting the mean and dividing by the standard deviation.

Observe method updates the mean and variance of a state vector with each new observation and clips the variance to prevent zero.

Update the online mean and variance to normalize each input state and return normalized values, then prepare a policy class to adjust the perceptron weights via perturbations to boost rewards.

Create a policy class using a perceptron with a weight matrix theta, initialize to zeros, and implement perturbation-based updates during training, exploring the policy space.

Implement version two of the algorithm by applying normalized state perturbations in 16 directions to the perceptron policy and evaluate rewards to identify the best direction.

Apply the update step of augmented random search by approximating the gradient with the method of infinite differences, using rewards from positive and negative perturbations to adjust theta.

Learn to explore ARS directions by running a full-episode exploration, normalizing states, evaluating perturbations, clipping rewards to prevent outliers, and accumulating rewards for policy training.

Train an artificial intelligence inside an environment using an augmented random search algorithm, with a reusable train function that accepts the environment, policy, normalises, and hyperparameters.

initialize 16 perturbation deltas and zeroed positive and negative rewards, sample perturbations from a normal distribution, and begin exploring a full episode for all directions.

Collect positive and negative rewards from six perturbation directions, then combine and scale them by their standard deviation to guide a one-step policy gradient update.

Concatenate positive and negative rewards into a single array, compute its standard deviation to guide a gradient descent update of the policy, then sort directions by the highest rewards.

Apply a gradient descent update to the policy using the rollout and the standard deviation of the rewards to improve weights and observe accumulated rewards at each training loop.

Apply the policy update with one gradient descent step, then test the updated policy over a full episode using the explore function with no perturbation and print the reward evaluation.

Celebrate completing the artificial intelligence course with a Tasmania travel compilation, thank learners for their effort, and invite reviews to help future students understand the course quality.