SELECTING REINFORCEMENT LEARNING ACTIONS USING GOALS AND OBSERVATIONS

摘要

Reinforcement learning using goals and observations selects actions to be performed by a reinforcement learning agent interacting with an environment. In the embodiments, the agent interacts with the environment to attempt to reach a predetermined set of target states of the environment. When the environment is a real-world environment and the agent is a robot interacting with the environment to accomplish a specific task, each target state can be a state in which a subtask has been completed by the robot, e.g., when the robot has moved an item from one location to another or has otherwise made progress toward completing the task. One of the methods includes receiving an observation characterizing a current state of the environment; receiving a goal characterizing a target state from a set of target states of the environment; processing the observation using an observation neural network to generate a numeric representation of the observation; processing the goal using a goal neural network to generate a numeric representation of the goal; combining the numeric representation of the observation and the numeric representation of the goal to generate a combined representation; processing the combined representation using an action score neural network to generate a respective score for each action in the predetermined set of actions; and selecting the action to be performed using the respective scores for the actions in the predetermined set of actions.