Strengths3
- Complete end-to-end training and inference workflow.
- Clear separation of concerns: network, agent, game logic, and training scripts.
- Demonstrates core DQN components correctly implemented.
Training Experiment · Python
DQN_Snake
This repository hosts a Deep Q-Network (DQN) agent designed to play the classic Snake game. The project leverages PyTorch for the deep learning component and Pygame for game rendering. This initiative served as my introduction to the fascinating world of reinforcement learning.
What it is
This repository implements a Deep Q-Network (DQN) agent to play the Snake game using PyTorch for the neural network and Pygame for rendering, including training and inference scripts.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
Where it counts
- Machine Learning90%
- AI Agents20%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
What the judge found
Read from the project's code at its scored commit.
Limitations
The owner keeps this project's limitations private.
Around it on the Machine Learning board
Whole board- 94PumpPerfect-PandasAIML Application · Python50.6ImplementationPetr SovadinaMachine Learning
- 95, shared rankchatbot-pipelineML Application · Python50.2ImplementationPop AlexandruMachine Learning
- 95, shared rankDQN_SnakeTraining Experiment · Python50.2ImplementationMohamed KhayatMachine Learning
- 97Linear-Regression-ScratchEducational Implementation · Jupyter Notebook50.0Implementationkamran akmalMachine Learning
- 98, shared rankImage-Classification-CIFAR100ML Application · Python49.7Implementationkamran akmalMachine Learning
