Learn AI Game Development using Python

Install and launch Spyder within the i games environment using Anaconda Navigator or the prompt, then explore its IDE features for writing and running Python code.

Explore how an agent navigates an environment by exploring states, receiving a positive reward, backtracking, and tracing paths to reach higher rewards.

Learn to draw shapes in pygame using the draw module, focusing on rect, line, and circle and how to plot a rectangle with x, y, width, height from top-left origin.

Apply rgb color codes to draw circles in pygame by selecting red, green, and blue values, setting circle position and radius on a surface, and choosing filled or outline thickness.

Implement a neural network with Keras by preparing input and preprocessing unstructured data, choosing a sequential or functional model, selecting an optimizer and loss, then training and evaluating.

Explore reinforcement learning basics by defining the optimal policy that maximizes return from rewards, contrast good and bad policies, and learn how Bellman equations drive value and policy iteration.

Use the Bellman equation to solve the Markov decision process and learn how reinforcement learning seeks the optimal policy by exploring states, maximizing rewards, and avoiding penalties.

Explore how the value function guides a learning agent from random exploration to reaching a goal state, using backtracking and the Bellman equation to define optimal actions.

Use the Bellman equation to compute the value of the current state by selecting the action that maximizes the reward plus gamma times the value of the next state.

Explore how the Bellman equation guides calculating rewards and state values to derive an optimal policy, using four available actions and the Markov chain principle to reach the goal.

Demonstrate how a deterministic environment yields action outcomes with 100 percent probability, and contrast it with a stochastic, non-deterministic environment where up actions lead to state transitions with defined probabilities.

Derive the q-learning equation for a deterministic environment from the Bellman equation, defining q(s,a) as reward plus gamma times the max next q(s',a').

Deep Q-learning uses neural networks to approximate Q-values for each state in a vector-represented environment, learning a policy by evaluating actions and temporal-difference updates.

Import numpy for numerical computation and create the mountain car v0 environment with gym.make. Set seeds, 110 for the environment and 10 for numpy, to ensure reproducible results.

Define a deep q-learning class with init that sets x n space and state space, and initialize epsilon, gamma, batch size, epsilon decay, learning rate, and a 100,000-length replay memory.

Build a neural network model with Keras to predict q-values for actions in a given state, using a deep Q-learning style sequential architecture with input, hidden, and output layers.

Build a replay buffer to store experiences as state, action, reward, next state, and done, with capped memory and oldest memory removal. Cover epsilon, gamma, batch size, and learning rate.

Act function selects the agent's action based on the current state, balancing epsilon-based exploration with exploitation, while the neural network predicts Q-values and uses experience replay to improve learning stability.

Apply the Q-learning update with a neural network to estimate q values from state, action, reward, and next state, using the Bellman equation and gamma to discount future rewards.

Learn to train a DQN by predicting on batch, updating target Q values for current states, replaying memory, and decaying epsilon to shift from exploration to exploitation.

Learn to train a DQN neural network using episodes in a mountain car environment, including state reshaping to column format, memory replay, and Q-learning updates to optimize cumulative reward.