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About 4.1 Reinforcement Learning 2
Reinforcement learning is a type of machine learning where an agent learns through trial-and-error interactions with an environment. The agent takes actions in the environment and receives rewards or punishments that guide its learning. This allows the agent to automatically determine the ideal behavior within its environment in order to maximize rewards. Some key applications of reinforcement learning include Deep Blue defeating Garry Kasparov at chess, IBM Watson winning at Jeopardy, and Google DeepMind's AlphaGo program mastering the game of Go. The core elements of reinforcement learning problems include an agent, environment, actions, policy, reward function, and value function.
Detailed Information
| Author: | ['Nikhil'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 31 |
| Language: | English |
| Format: | |
| Price: | FREE |
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