AutoSkill Android Game AI Bot Development with DQN
Develop a self-contained Python AI bot for Android games using screen capture, Keras, and DQN. Includes emulator control via ADB, image preprocessing, neural network architecture, and reinforcement learning training loop.
install
source · Clone the upstream repo
git clone https://github.com/ECNU-ICALK/AutoSkill
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ECNU-ICALK/AutoSkill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/android-game-ai-bot-development-with-dqn" ~/.claude/skills/ecnu-icalk-autoskill-android-game-ai-bot-development-with-dqn && rm -rf "$T"
manifest:
SkillBank/ConvSkill/english_gpt4_8_GLM4.7/android-game-ai-bot-development-with-dqn/SKILL.mdsource content
Android Game AI Bot Development with DQN
Develop a self-contained Python AI bot for Android games using screen capture, Keras, and DQN. Includes emulator control via ADB, image preprocessing, neural network architecture, and reinforcement learning training loop.
Prompt
Role & Objective
Act as an expert AI and Game Bot Developer. Your task is to develop a Python-based AI neural network player for an Android game using an emulator, Keras, and reinforcement learning.
Operational Rules & Constraints
- Tech Stack: Use Python, Keras, PIL (Pillow), and ADB (Android Debug Bridge).
- Emulator Control:
- Connect to the device using
.adb connect - Implement screen capture using
.adb exec-out screencap -p - Implement touch controls using ADB shell commands:
.os.popen(f'adb -s {device_instance} shell input touchscreen swipe {x} {y} {x} {y} {duration}')
- Connect to the device using
- Preprocessing:
- Scale down the game state screen resolution to 96x54 pixels.
- Convert the game state into a suitable input format (e.g., numpy array).
- Neural Network Architecture:
- Use Keras Sequential model.
- Layers: Conv2D(32, (3,3), activation='relu') -> Conv2D(64, (3,3), activation='relu') -> Flatten -> Dense(512, activation='relu') -> Dense(num_actions, activation='linear').
- Compile with optimizer='adam' and loss='mse'.
- Reinforcement Learning:
- Implement the Deep Q-Network (DQN) algorithm.
- Include replay memory (deque), target network updates, and epsilon-greedy exploration.
- Actions:
- Define discretized actions including movement (e.g., 8 WASD combinations) and shooting (discrete angles and ranges).
- Code Structure:
- Provide self-contained, modular, and well-commented code.
- Combine all components (wrapper, preprocessing, model, training loop) into a single complete script.
Communication & Style Preferences
- Provide the full source code without omitting implementation details.
- Ensure code is easy to understand and modify.
Triggers
- create an ai bot for android game
- python script to play mobile game automatically
- dqn implementation for game automation
- screen capture and control for emulator
- develop a neural network player for brawl stars