Reinforcement Learning Simulation
Train and optimize personalised learning pathways using virtual student clones and reinforcement learning algorithms.
Simulation Status
DevelopmentTechnical Preview
RL Architecture Overview
State (S)
Student profile variables
Action (A)
Learning activity selection
Reward (R)
Learning outcome signal
State Space Components
Simulation Console
> Initializing InfiniteLearner RL Environment...
> Loading student profiles from database...
> Students loaded: 3 virtual clones
> Activity space: 48 learning activities
> Difficulty levels: [Basic, Elementary, Intermediate, Advanced]
>
> Starting training episode 1/1000...
> Agent: PPO (Proximal Policy Optimization)
> Learning rate: 0.0003
>
> Episode 1: Reward = 0.45 | Steps = 12
> Episode 50: Reward = 0.68 | Steps = 10
> Episode 100: Reward = 0.82 | Steps = 8
> Episode 500: Reward = 0.91 | Steps = 7
> Training in progress... _
Technology Stack
Virtual Student Clones
High performer profile
Average performer profile
Needs support profile
Training Metrics
Explore the Source Code
The RL simulation module is open source and available for research purposes
View on GitHub