Reset Function in RL : Initial State or Current State?

Hey everyone,

I’m new to Reinforcement Learning. I’m currently working on implementing a RL environment to control the temperature of a room using a thermostat. I’m at a crossroads regarding the reset function and would love some insights.

The environment represents a room with a thermostat, where the goal is to maintain the temperature at a desired setpoint while minimizing energy consumption. The state of the environment is represented by the current temperature and the energy consumption, and actions include adjusting the thermostat settings to increase or decrease the temperature setpoint.

I thought of 2 approaches :

Reset to Initial State: This option involves resetting the environment to its initial configuration at the beginning of each episode, where the temperature is set to a predefined starting value. (Setting to the minimal state). Reset to Current State: Alternatively, the reset function could return the environment to the current state of the room..

I’m particularly drawn to the idea of resetting to the current state because it aligns with my approach in the agent’s decision-making process. In each step, when the agent chooses an action, I’m planning to check if the agent can perform that action based on the current state represented in the state vector then give positive reward, else give negative reward.

Question: Given this environment, which approach do you think would be more suitable for the reset function in my RL algorithm? Are there any additional considerations I should take into account?

Thanks in advance for your help!

submitted by /u/Few-Papaya101
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