N-step return-based implicit regularization offline reinforcement learning method and apparatus

An offline reinforcement learning apparatus for n-step return-based implicit regularization comprising:

• a processor;
• and
• a memory connected to the processor,
• wherein the memory comprises program instructions, in response to being executed by the processor, perform operations comprising,
• sampling, among datasets collected in a preset domain, some datasets including state, action, state at the next time point, reward, and return in n-step,
• calculating an objective function of a state value model that evaluates a value of a specific state using the sampled data set to update a parameter of the state value model,
• setting a TD (temporal difference) target based on the state value model,
• calculating an objective function of a state-action value model that evaluates a value of a specific state and action pair based on the set TD target and updating the parameter of the state-action value model,
• calculating, after updating the state-action value model, an objective function of a policy model for determining an action according to a given state and updating the parameter of the policy model.