What techniques can you use to program realistic AI opponents for esports games?

2 Behavior Trees

A behavior tree, or BT, is a hierarchical structure that represents the logic and the goals of the AI. Each node in the tree is either a task, a condition, or a control flow. A task is an atomic action that the AI can perform, such as move, shoot, reload, etc. A condition is a boolean expression that evaluates the state of the world or the AI, such as isEnemyVisible, isHealthLow, isAmmoEmpty, etc. A control flow is a node that determines how to execute its child nodes, such as sequence, selector, parallel, etc. A BT can be implemented using custom classes or frameworks in most programming languages. A BT is flexible and modular, but it can become difficult to maintain and test if there are too many nodes and branches.

3 Neural Networks

A neural network, or NN, is a mathematical model that mimics the structure and the function of the biological brain. It consists of a network of interconnected units called neurons, which can process and transmit information. Each neuron has a set of inputs, weights, biases, and an activation function that determines its output. A NN can be trained using supervised or unsupervised learning methods to learn patterns and relationships from data. A NN can be implemented using libraries or frameworks such as TensorFlow, PyTorch, or Unity ML-Agents in most programming languages. A NN is powerful and adaptable, but it can be hard to interpret and optimize.

4 Reinforcement Learning

Reinforcement learning, or RL, is a type of machine learning that deals with learning from trial and error. It involves an agent, an environment, a policy, a reward, and a value function. The agent is the AI that interacts with the environment, which is the game world. The policy is the strategy that the agent follows to choose its actions. The reward is the feedback that the agent receives from the environment for each action. The value function is the estimation of the future rewards that the agent can expect from each state. The agent can learn its policy and value function using algorithms such as Q-learning, SARSA, or DQN. RL can be implemented using libraries or frameworks such as TensorFlow, PyTorch, or Unity ML-Agents in most programming languages. RL is dynamic and autonomous, but it can be slow and unstable.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?