Deep reinforcement learning framework

Abstract

A deep reinforcement learning framework according to an embodiment may include a reinforcement learning environment that provides an environment with which an agent may interact; a policy network that learns an optimal policy through trial and error in which the agent selects an action based on a given state in the reinforcement learning environment and obtains a reward as a result of the action; a memory for reproduction that stores information about a state, action, reward, and next state generated by the agent interacting with the reinforcement learning environment; an extrinsic uncertainty recognition unit that determines extrinsic uncertainty based on the agent's metacognitive ability and detects a new state to provide an additional exploration reward; an intrinsic uncertainty recognition unit that evaluates intrinsic uncertainty of transactions generated by the policy network; an uncertainty data filtering unit that selects a transaction with high uncertainty based on an evaluation result of the intrinsic uncertainty recognition unit; and a memory for reproduction reconstruction unit that reconstructs the memory for reproduction based on the selected transaction to optimize repeated learning of the agent.

Dependencies are inferred from the claim text.