Deep Q-learning for playing flappy bird game

"Three screenshots of the game Flappy Bird at three different difficulties (easy, medium, hard) respectively"

Deep Q-learning for playing flappy bird game

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A Deep Q-Network (DQN) implementation for the FlappyBird-v0 environment using PyTorch. The agent is capable of learning to play Flappy Bird, demonstrating variable rewards due to the stochastic nature of the game and reinforcement learning. Includes training, model saving, and inference scripts with configurable hyperparameters.

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Deep Q-learning for playing Flappy Bird

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An AI agent created by Claude Code that learns to play Flappy Bird using Deep Q Learning in PyTorch.

Reinforcement Learning Agent to play Flappy Bird - Usage of PyTorch and Deep Q-Network

Deep Q-Learning implementation (PyTorch) following course videos. Includes CartPole and Flappy Bird DQN agents.

Deep Q Learning Model with PyTorch made to play Flappy Bird (my first attempt with RL)

An AI that plays Flappy Bird using Deep Q Learning with pytorch and a pygame environment.

A Deep Q-Network (DQN) agent trained to play Flappy Bird using reinforcement learning with PyTorch and Gymnasium.

Reinforcement learning agent trained with Deep Q-Network (DQN) to play Flappy Bird using Python, PyTorch, and Pygame.

Developed and trained a Deep Q-Learning (DQN) using Convolutional Neural Network (CNN) for processing the Flappy Bird game using PyTorch framework!

Here I implemented DQN algorithm from scratch, combining deep learning with q-network in PyTorch, and then used it to train the Flappy Bird Game.

Flappy Bird AI using Deep Q-Learning (DQN) built with PyTorch and Pygame. Includes experience replay, target network, GPU training, and comparison between 200 episodes and 600 episodes trained models.

A Deep Q-Network (DQN) based reinforcement learning agent that learns to play Flappy Bird using PyTorch and Gymnasium. The model leverages experience replay and target networks to achieve stable learning and improved performance over time.

An implementation of a Reinforcement Learning agent trained to play Flappy Bird using a Deep Q-Network (DQN). This project explores the intersection of computer vision (or state-based inputs) and autonomous decision-making. Built with Python, Pygame, and [PyTorch/TensorFlow].