Discover how intelligent agents learn through trial and error, making decisions based on rewards and penalties to master complex tasks.
The continuous cycle of learning through interaction
Agent
Environment
Reward
The learner that makes decisions and takes actions
The world the agent interacts with
Moves or decisions made by the agent
Feedback signal for actions taken
Strategy the agent learns over time
Watch the agent learn to navigate the maze
See how the agent improves over time
Random exploration, many mistakes
Learning patterns, fewer errors
Optimal policy, efficient paths
Different ways agents receive feedback
Reward for correct actions
Example: Reaching Goal
The agent receives a positive reward when it successfully completes a task, encouraging that behavior.
Penalty for wrong actions
Example: Hitting Obstacle
The agent receives a negative reward when it makes a mistake, discouraging that behavior.
Exploration: Try new actions to discover potentially better strategies
Where RL is making an impact today
Chess, Go, Atari games, and more
Autonomous navigation and decision making
Robot manipulation and locomotion
Personalized content suggestions
Smart grid optimization
Financial decision making
Experiment with different settings
How quickly the agent updates its knowledge
Importance of future rewards vs immediate
Probability of random exploration
Reward for reaching the goal
Balanced learning speed. Agent updates gradually.
Values future rewards highly. Long-term planning.
Mostly exploits, occasionally explores.
What you've learned about Reinforcement Learning
You now understand the fundamentals of Reinforcement Learning