Julian Z.

> I am curious, what is the motivation of using MARL? To simulate a real market of multiple players, each of which adopts a different strategy, MARL seems to be...

> env_config = { > "price_array": price_array, > "tech_array": tech_array, > "turbulence_array": turbulence_array, > "if_train": False, > } > env_instance = env(config=env_config) >if drl_lib == "elegantrl": > from finrl.agents.elegantrl.models import...

> 1. Agent's state includes its own state and state of market. An action changes the "inner state" of the agent, NOT the state of a market. > 2. You...

> we have not updated it. pls use env_stocktrading.py ElegantRL only works with env_stocktrading_np so I have done all the work with env_stocktrading_np. Is it wrong? Thanx.

> we have not updated it. pls use env_stocktrading.py I have revised env_stocktrading_np, and firmly believe it is correct. env_stocktrading.py involves too many parameters, can you give an example of...

> The initial state of the model is initialized randomly. This randomness can lead to inconsistent results across different runs, especially if no random seed is provided in the code....

> Thank you for reaching out! It's important to note that randomness is inherent in all deep learning models and is not a bug that requires fixing. Rather, it's a...

> When you input data from the previous day or the market's closing moment into the predictive model, it generates an output suggesting the action to take for the following...