Hello everyone,
I have two issues:
I wasn’t able to set up the environment so that the agent takes 24 different actions over the course of a day, meaning the agent takes one action every hour. As a workaround, I decided to train agents by the hour.
The second issue, which is the reason for my question, arises after training the agent. When I test the efficiency of its decision-making and run the simulation part of the RL Toolbox, I notice that the agent always takes the same action regardless of the state of the environment. This leads me to believe that the training process determines the best action for a set of states, which is not what I want. I want the agent to take the correct action for different states. I’ve been analyzing my environment code but can’t figure out why the agent behaves this way.
Thank you in advance.
BryanHello everyone,
I have two issues:
I wasn’t able to set up the environment so that the agent takes 24 different actions over the course of a day, meaning the agent takes one action every hour. As a workaround, I decided to train agents by the hour.
The second issue, which is the reason for my question, arises after training the agent. When I test the efficiency of its decision-making and run the simulation part of the RL Toolbox, I notice that the agent always takes the same action regardless of the state of the environment. This leads me to believe that the training process determines the best action for a set of states, which is not what I want. I want the agent to take the correct action for different states. I’ve been analyzing my environment code but can’t figure out why the agent behaves this way.
Thank you in advance.
Bryan Hello everyone,
I have two issues:
I wasn’t able to set up the environment so that the agent takes 24 different actions over the course of a day, meaning the agent takes one action every hour. As a workaround, I decided to train agents by the hour.
The second issue, which is the reason for my question, arises after training the agent. When I test the efficiency of its decision-making and run the simulation part of the RL Toolbox, I notice that the agent always takes the same action regardless of the state of the environment. This leads me to believe that the training process determines the best action for a set of states, which is not what I want. I want the agent to take the correct action for different states. I’ve been analyzing my environment code but can’t figure out why the agent behaves this way.
Thank you in advance.
Bryan agent drl, action agent, rl toobox MATLAB Answers — New Questions

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