Game AI and Reinforcement Learning

Core Idea: This course explores the fascinating world of game AI, from classic algorithms to the cutting-edge field of reinforcement learning. You’ll learn how to build agents that can make intelligent decisions in game environments.

1. Classic Game AI Algorithms

1.1 Minimax

Minimax is a decision rule used in two-player, zero-sum games (like Tic-Tac-Toe or Chess). The goal is to minimize the possible loss for a worst-case (maximum loss) scenario. It’s a recursive algorithm that explores the game tree to find the optimal move.

1.2 Heuristics

For complex games, exploring the entire game tree is computationally infeasible. Heuristics are rules of thumb or evaluation functions that estimate the value of a game state without full exploration. A good heuristic is crucial for building a strong game AI.

2. Introduction to Reinforcement Learning (RL)

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward.

• Agent: The learner or decision-maker.
• Environment: The world the agent interacts with.
• Action: A move the agent can make.
• State: The current situation of the agent in the environment.
• Reward: The feedback the agent receives for its actions.

3. Q-Learning

Q-learning is a model-free reinforcement learning algorithm. It learns a policy, which tells an agent what action to take under what circumstances. It does this by learning a Q-function, which represents the expected future reward for taking a certain action in a certain state.

4. Deep Q-Networks (DQN)

A Deep Q-Network is a neural network that is used to approximate the Q-function. This allows Q-learning to be applied to problems with very large state spaces, such as the screen of a video game.

# A simplified DQN structure
model = keras.Sequential([
    layers.Dense(units=64, activation='relu', input_shape=[state_size]),
    layers.Dense(units=64, activation='relu'),
    layers.Dense(units=action_size, activation='linear'),
])

5. The Exploration-Exploitation Trade-off

A fundamental challenge in RL is the trade-off between exploration (trying new actions to discover better rewards) and exploitation (using the knowledge already gained to maximize reward). An epsilon-greedy strategy is a common approach, where the agent explores with a small probability (epsilon) and exploits the rest of the time.

Key Takeaways:

• Classic game AI algorithms like Minimax provide a foundation for decision-making in games.
• Reinforcement learning offers a powerful framework for training agents to learn optimal behaviors through trial and error.
• Q-learning and DQNs are fundamental algorithms in the RL toolkit.
• The exploration-exploitation trade-off is a key consideration when designing RL agents.