snake ga
Basic Information
This repository provides an implementation of an AI agent that learns to play the classic Snake game using Deep Q-Learning. It is intended as a concrete example of reinforcement learning applied to a simple game environment, supplying code to define the game environment, the agent architecture that approximates Q-values, and routines to train and evaluate the agent over episodes. The codebase is useful for running experiments that demonstrate how a neural-network-based Q-learner can improve performance through iterative experience. The project targets readers interested in hands-on reinforcement learning and game-based agent training rather than production deployment.
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App Details
Features
Implements the Deep Q-Learning approach to train an agent to play Snake. Provides a runnable Snake game environment coupled with agent code that interacts with the environment to collect experience and update a Q-function approximator. Includes training and evaluation routines to run episodes and observe learning progress. Supplies example code and experiment setup to replicate basic training workflows and to observe an agent improving its gameplay. Focuses on reinforcement learning concepts applied to a simple, self-contained game domain rather than broader tooling or infrastructure.
Use Cases
Helps learners and researchers study reinforcement learning by offering a straightforward, game-based example of Deep Q-Learning in action. It serves as an educational reference and a reproducible baseline for experimenting with hyperparameters, network architectures, or exploration strategies. Hobbyists can use it to see a full train-evaluate loop and to observe learned behaviors in a controlled environment. Developers and students can extend the code to try alternative RL algorithms, add instrumentation for performance analysis, or adapt the environment to other grid-based games for comparative experiments.
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