Reinforcement Learning (Learning)

1 Concepts

1.1  State

The status of the agent with respect to the environment. Grid example, the location of the agent is state.

State space. The set of all states. S

1.2 Action

For each state, there are several possible actions. Grip dxample : 4 movements.

1.3 State transition

When taking an action, the agent moves from one state to another state. Such a process is called state transition.

$$ S_1 \xrightarrow{a_2} S_2 $$

State transition probability: 

$$ p(s_2 | s_1, a_2) = 1 $$

$$ p(s_i | s_1, a_2) = 0 \quad \forall i \neq 2 $$

1.4 Policy

Policy tells the agent what actions to take at a state. 

$$ \pi(a_1 | s_1) = 0 $$

1.5 Reward

 

https://www.bilibili.com/video/BV1sd4y167NS?spm_id_from=333.788.player.switch&vd_source=8c42b55896f8a24fb4f65b7a3383faa1&p=3