Understanding Reinforcement Learning
Reinforcement Learning is an exciting area of machine learning where agents learn to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
In the world of Froges, imagine a 🐸 learning to jump across lily pads to catch the juiciest flies, improving its technique with every leap!
Core Elements of Reinforcement Learning:
- Agent: The learner or decision maker, like our froge.
- Environment: The space in which the agent operates, such as the lily ponds.
- Action: The moves the agent makes, equivalent to our froge's jumps.
- Reward: The feedback from the environment, such as catching a fly.
- Policy: The strategy used by the agent, like planning the best path across the pond.
To dive deeper, check out another delightful read on Deep Divergence into Froge RL!