Deepmind technologies limited (20240177002). CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING simplified abstract

CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING

Organization Name

deepmind technologies limited

Inventor(s)

Timothy Paul Lillicrap of London (GB)

Jonathan James Hunt of London (GB)

Alexander Pritzel of London (GB)

Nicolas Manfred Otto Heess of London (GB)

Tom Erez of London (GB)

Yuval Tassa of London (GB)

David Silver of Hitchin (GB)

Daniel Pieter Wierstra of London (GB)

CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240177002 titled 'CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING

The patent application describes methods, systems, and apparatus for training an actor neural network to select actions for an agent interacting with an environment. This involves updating the parameters of the actor neural network based on experience tuples and using a critic neural network to determine errors and update its parameters as well.

• Obtaining a minibatch of experience tuples
• Updating parameters of the actor neural network based on experience tuples
• Processing training observations and actions using a critic neural network
• Determining neural network outputs and target outputs for experience tuples
• Updating parameters of the critic neural network using errors between target outputs and neural network outputs
• Updating parameters of the actor neural network using the critic neural network

Potential Applications: - Autonomous vehicles - Robotics - Gaming industry

Problems Solved: - Improving decision-making processes in autonomous systems - Enhancing the efficiency of agent interactions with environments

Benefits: - Increased accuracy in action selection - Improved performance of agents in dynamic environments

Commercial Applications: Title: "Enhancing Decision-Making in Autonomous Systems with Actor Neural Networks" This technology can be used in autonomous vehicles, robotics, and gaming industries to improve decision-making processes and enhance the efficiency of agent interactions with environments.

Questions about the technology: Question 1: How does the actor neural network differ from the critic neural network in this system? Answer: The actor neural network is responsible for selecting actions, while the critic neural network evaluates the actions chosen by the actor.

Question 2: What are the key advantages of using neural networks in training agents for interacting with environments? Answer: Neural networks can learn complex patterns and relationships in data, allowing agents to make more informed decisions in dynamic environments.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. one of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.