Definition¶
Tags¶
Reinforcement learning, Training
Additional Notes¶
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
Think of it like teaching a dog new tricks:
- The dog (agent) performs actions
- You give treats or praise (rewards) for good behavior
- The dog learns which actions lead to treats
- Over time, it figures out the best sequence of actions to get rewards Key Components:
The Learning Process:
Common Approaches:
Q-Learning: Learns the value of actions in different states
Policy Gradient: Directly learns the best policy
Deep RL: Combines deep neural networks with RL
Model-Based RL: Learns a model of the environment Real-World Applications:
Game playing (AlphaGo, OpenAI Five)
Robotics and robot control
Resource management
Recommendation systems
Autonomous vehicles
Trading strategies Key Challenges:
Exploration vs. Exploitation trade-off
Delayed rewards (credit assignment problem)
Large state/action spaces
Sample efficiency
Stability during training The power of RL lies in its ability to learn through trial and error, discovering solutions that might not be obvious to human programmers. Unlike supervised learning, which requires labeled examples, RL can learn from raw experience in the environment.