AI/ML
Completed
Featured
Play-QWOP-by-Using-AI-Agent
Reinforcement Learning Game AI
Use reinforcement learning to play QWOP with python3 by using tensorflow2 and reach the goal!
Project Gallery
Overview
An innovative reinforcement learning project that teaches an AI agent to play the notoriously difficult QWOP game using TensorFlow 2. The project demonstrates advanced RL techniques including actor-critic methods and policy gradient optimization.
Key Features
Actor-critic RL implementation
QWOP game environment integration
Training visualization
Model checkpointing
Performance metrics tracking
Challenges
Complex game physics simulation
Reward function design
Training stability
Action space optimization
Solutions
Implemented custom game environment
Designed shaped reward functions
Added training stabilization techniques
Optimized action space representation
Results & Impact
Successfully trained QWOP-playing agent
Achieved consistent goal completion
Demonstrated RL effectiveness
Educational value for RL community
Project Info
Role
Creator & Maintainer
Timeline
2019 - 2020
1 year
Technologies
PythonTensorFlowReinforcement LearningOpenAI GymNeural Networks