📚 Reinforcement Learning Overview
Explore the history and fundamental concepts of reinforcement learning
🕰️ RL History Timeline
📖 RL Glossary
🎮 Choose Your Learning Environment
Select an environment to train your reinforcement learning agent
🧠 Agent Trainer
Train your agent with Q-learning and watch it learn in real-time
⚙️ Training Parameters
🏁 Gridworld Environment
Episode: 0
Success Rate: 0%
Total Reward: 0
📈 Training Progress
🔥 Q-Table Heatmap
⚡ Exploration vs Exploitation
Understand the epsilon-greedy strategy and its impact on learning
🎚️ Epsilon Control
🔍 Exploration
50%
🎯 Exploitation
50%
🤔 Decision Making Process
Current State: S1
🔍 Explorations
0
🎯 Exploitations
0
💡 Understanding the Tradeoff
Exploration means trying new actions to discover potentially better strategies, even if they seem suboptimal based on current knowledge.
Exploitation means using current knowledge to choose actions that are known to be good.
The epsilon-greedy strategy balances this by taking random actions with probability ε and greedy actions with probability (1-ε).
🎯 Test Your Knowledge
Quiz yourself on reinforcement learning concepts and explore additional resources
Question 1 of 10