Reinforcement Learning Term


### States
- environment state - environment representation
- **agent state** - agent representation - most used state
- information state (Markov state) - probability from start to this state = probability from previous state to this state → state is Markov (independant)

### Others
- history - sequence of observation, action, reward
- value iteration
> ![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2F741e07fc-f795-4de3-a9b3-b0e5ffdcd557%2FUntitled.png?table=block&id=ec29a645-745b-4a6e-aeaf-f05124c05ddb&cache=v2)
- policy iteration
- MDP - policy and value
> ![](https://www.notion.so/image/https%3A%2F%2Fs3-us-west-2.amazonaws.com%2Fsecure.notion-static.com%2Fe77bfc9b-5f87-4825-8985-dd921d1523e7%2FUntitled.png?table=block&id=ee5c5148-bb6c-4042-8db8-48f906b174fc&cache=v2)

> Both value iteration and policy iteration compute the same thing (all optimal values)












