Deepmind technologies limited (20240320506). RETRIEVAL AUGMENTED REINFORCEMENT LEARNING simplified abstract

RETRIEVAL AUGMENTED REINFORCEMENT LEARNING

Organization Name

deepmind technologies limited

Inventor(s)

Anirudh Goyal of London (GB)

Andrea Banino of London (GB)

Abram Luke Friesen of London (GB)

Theophane Guillaume Weber of London (GB)

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Nan Ke of London (GB)

Simon Osindero of London (GB)

Timothy Paul Lillicrap of London (GB)

Charles Blundell of London (GB)

RETRIEVAL AUGMENTED REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320506 titled 'RETRIEVAL AUGMENTED REINFORCEMENT LEARNING

The patent application describes methods, systems, and apparatus for controlling a reinforcement learning agent in an environment using a retrieval-augmented action selection process.

• Receiving a current observation characterizing the state of the environment.
• Processing an encoder network input to determine a policy neural network hidden state corresponding to the current observation.
• Maintaining multiple trajectories generated by the agent interacting with the environment.
• Selecting one or more trajectories from the maintained trajectories.
• Updating the policy neural network hidden state using data from the selected trajectories.
• Generating a policy output specifying an action for the agent based on the updated hidden state.

Potential Applications: - Autonomous vehicles - Robotics - Gaming AI - Industrial automation - Healthcare diagnostics

Problems Solved: - Enhancing decision-making capabilities of AI agents - Improving task performance in dynamic environments - Increasing efficiency and accuracy of AI systems

Benefits: - Enhanced learning and adaptation in complex environments - Improved decision-making processes - Increased task performance and efficiency

Commercial Applications: - AI-driven customer service systems - Autonomous drones for delivery services - Smart manufacturing processes - Personalized healthcare diagnostics - Automated trading systems

Questions about the technology: 1. How does this technology improve the efficiency of AI systems in dynamic environments? 2. What are the potential implications of this technology in the field of robotics and automation?

Frequently Updated Research: - Stay updated on advancements in reinforcement learning algorithms and neural network architectures to enhance the performance of the described system.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for controlling a reinforcement learning agent in an environment to perform a task using a retrieval-augmented action selection process. one of the methods includes receiving a current observation characterizing a current state of the environment; processing an encoder network input comprising the current observation to determine a policy neural network hidden state that corresponds to the current observation; maintaining a plurality of trajectories generated as a result of the reinforcement learning agent interacting with the environment; selecting one or more trajectories from the plurality of trajectories; updating the policy neural network hidden state using update data determined from the one or more selected trajectories; and processing the updated hidden state using a policy neural network to generate a policy output that specifies an action to be performed by the agent in response to the current observation.