Learn AI Game Development using Python

Install and set up Anaconda to run AI game development projects using Python. Learn to download, install, configure Anaconda Prompt and Navigator, create a new environment, and launch Spyder.

Learn to create a conda environment for AI game development by using Anaconda navigator or prompt, name it AI games, set python 3.6, and activate it for the course.

Install all dependencies from requirements.txt for AI game development, including TensorFlow, Keras, gym, matplotlib, OpenCV, and pygame, by activating the environment and running pip install -r requirements.txt.

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 artificial intelligence creates data through environment interactions, advancing from deep neural networks and reinforcement learning to robotics, self-driving cars, and smarter applications like recommendation engines and games.

Explore reinforcement learning, where agents learn by interacting with an environment, creating data internally, mimicking animal learning and earning rewards to excel in simulations without analytic solutions.

Explore the environment by treating each box as a state and the collection of states as the environment, then train your agent through exploration to identify good and bad states.

Explore how rewards guide an agent, showing negative rewards punish bad states like traps while positive rewards reward good states such as the path to cheese, guiding agent behavior.

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

Explore the Markov property and Markov chain, learning how future states depend on the current state through transition probabilities and matrices, with weather forecasting examples.

Learn how the Markov reward process extends Markov chains by attaching a reward function to each state. Identify state, transition probability, and reward values across cloudy, rainy, and windy scenarios.

Explore the Markov decision process, extending the Markov reward process by incorporating states, actions, transition probabilities, and rewards, illustrated through a gridworld example of moving up, down, left, or right.

learn how to draw rectangles in Pygame by defining a display surface, setting an origin at the top-left, and specifying x, y, width, and height with an RGB color.

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.

Learn how to draw shapes in pygame by using draw functions to render rectangles, lines, circles, and polygons on a display surface, with position, size, and color considerations.

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.

Learn a practical Pygame template for building Python games, covering a main game loop, window creation, event handling, and screen updates.

Define a player as a rectangle and render it on a pygame window using rect, specifying a surface, x, y, width, height, and color.

Move a 50 by 50 rectangle with WASD in a pygame-based game, updating x and y per key press and noting boundary behavior and need for FPS for smooth motion.

Learn to use pygame with a 30 fps update loop to smoothly move a keyboard-controlled rectangle and keep it within the window boundaries.

Learn how to confine a player inside a Pygame window by restricting x and y coordinates with boundary checks, using a 50 pixel rectangle and keyboard input.

Explore TensorFlow, a fast open-source library from Google Brain for deep learning, using data flow graphs and tensors to build neural networks with input, hidden, and output layers.

Explore tensor ranks from scalar to tensor and how TensorFlow uses a computational graph, a convolution neural network, and building blocks like matmul and ReLU.

Learn the core elements of TensorFlow by building and executing a computational graph, using constants, placeholders, and variables, and evaluating with a session.

Learn how TensorFlow uses data flow graphs and tensors to build and run models, with hello world examples, and cover variables, constants, placeholders, initialization, and session-based execution.

Explore Keras, the high level deep learning library that runs on top of TensorFlow. Build neural networks by stacking layers, and leverage its modular, open source backend for efficient backpropagation.

Learn Keras models by examining sequential architectures for convolutional neural networks and functional models with multi-input multi-output designs, shared weights, and domain adaptation for game AI.

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 gamma discount factor in the Bellman equation shapes state values, guiding the agent toward the goal by selecting actions that maximize future rewards.

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.

Explore how the Bellman equation extends to non-deterministic environments by incorporating probabilistic transitions, rewards, and discounting to compute state values from multiple next states.

Learn q-learning, where q-values measure the quality of actions in a state. Compare this with value functions that assess state quality and guide action selection via the highest q-value.

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').

Explain the Q-learning update rule for both deterministic and non-deterministic environments, incorporating reward, discount factor, probabilities, and the max future Q-value to value state-action pairs.

learn temporal difference learning to update q-values in both deterministic and stochastic environments, using the bellman equation, rewards, gamma, and a learning rate alpha during training through exploration.

Download the Q-learning practical resources and set up the environment with TensorFlow Keras and gym. Use Spyder to write and run the train.py Q-learning code in your game.

learn to work with the python-based environment for q-learning game development, using environment.py and train.py to model a 5x7 grid world with rewards, events, and obstacles.

Learn AI game development using Python: define hyperparameters for q-learning in a game environment by implementing train.py, focusing on gamma (discount factor), alpha (learning rate), and epochs.

Initialize the environment and q-table from the reward board, then show how current state maps to next states, with penalties for staying and rewards like the finish-line 1000.

Identify valid moves from the current state using the cue table, with rewards for transitions, while excluding walls and diagonals, and build the possible states by checking nonzero rows.

Develop a function to compute the maximum q value and its state index from recorded q-values. Initialize with negative infinity to accommodate possible negative q-values, and ensure actions are valid.

Kick off Q-learning phase 1 by training for a fixed number of epochs, selecting random non-wall starting states and evaluating possible actions toward next states using the Q-table.

Explore q-learning phase 2 by selecting a random action from multiple options, evaluating the reward, and updating the q-table using the max q value in a temporal-difference framework.

Apply temporal difference learning and the Bellman equation to update the Q table using the reward, gamma, and the maximum Q value of the next state.

Develop an agent that uses temporal-difference q-learning to update a cue table and select actions that maximize rewards, guiding the player from the starting position toward the goal.

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.

Explore action selection policies for deep Q-learning, and learn how to choose the action with the highest Q-value using epsilon-greedy, epsilon-soft, or softmax.

Learn how deep convolution Q-learning extends deep q-learning by processing game environments as images through convolution and pooling, enabling effective q-value estimation and action selection in complex worlds.

Implement deep q-learning using gym environments to train an AI on the mountain car task. Explore discrete actions, observation space, and memory replay to master environment setup and training workflow.

Implement a deep q-learning (DQN) algorithm in Python by importing gym, random, and Keras, building a TensorFlow-backed neural network, and applying it to the mountain car environment.

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.

Implement replay using memory of experiences (state, action, reward, next state, done) to train the Q-learning network with a sampled mini-batch.

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.

Implement get_reward in the DQN class using the state's position and velocity to reward reaching the goal (ten when position ≥ 0.5) and boost learning via memory replay.

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.

Implement a random policy by sampling actions from the action space, run episodes, handle termination, start new episodes, and train a ddqn model while plotting loss with matplotlib.