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RL-PID

RL Environments

The reinforcement learning environments defines the world where the RL-agent(decision maker) gain experience by taking action that changes the environment, this action could induce a positive change environment or even become adverse.

Rewards

The changes in the system(environment) is provided to RL-agent via. `scalar` quantity, and the RL-agents updates itself based on this `reward` signal.

graph LR
A[Agent] -- action --> B((Environemt))
B -- reward, state --> A
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