Deepmind technologies limited (20240265263). METHODS AND SYSTEMS FOR CONSTRAINED REINFORCEMENT LEARNING simplified abstract

METHODS AND SYSTEMS FOR CONSTRAINED REINFORCEMENT LEARNING

Organization Name

deepmind technologies limited

Inventor(s)

Theodore Harris Moskovitz of London (GB)

Brendan Timothy O'donoghue of London (GB)

Tom Ben Zion Zahavy of London (GB)

Johan Sebastian Flennerhag of London (GB)

Vivek Veeriah Jeya Veeraiah of London (GB)

Satinder Singh Baveja of Ann Arbor MI (US)

METHODS AND SYSTEMS FOR CONSTRAINED REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240265263 titled 'METHODS AND SYSTEMS FOR CONSTRAINED REINFORCEMENT LEARNING

Simplified Explanation: The patent application describes a method for training a policy model, such as a neural network, to control an agent interacting with an environment to perform a task subject to constraints.

Key Features and Innovation:

• Iterative training of a policy model for action selection.
• Constraints with associated reward and multiplier variables.
• Generation of a mixed reward function in each iteration.
• Estimation of rewards and constraint values based on the policy model.

Potential Applications: This technology could be applied in various fields such as robotics, autonomous vehicles, and game AI.

Problems Solved: This technology addresses the challenge of training an agent to perform tasks while adhering to constraints.

Benefits:

• Improved performance of agents in complex environments.
• Efficient training of policy models.
• Enhanced control over agent behavior.

Commercial Applications: Potential commercial applications include autonomous systems, industrial automation, and smart home devices.

Prior Art: Prior research in reinforcement learning and policy optimization may be relevant to this technology.

Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms and policy optimization techniques.

Questions about the Technology 1. How does this method improve the efficiency of training policy models? 2. What are the potential real-world applications of this technology?

Original Abstract Submitted

a method is described for iteratively training a policy model, such as a neural network, of a computer-implemented action selection system to control an agent interacting with an environment to perform a task subject to one or more constraints. the task has a reward associated with performance of the task. each constraint limits to a corresponding threshold the expected value of the total of a corresponding constraint function which if the future actions of the agent are chosen according to the policy model, and each constraint is associated with a corresponding multiplier variable. in each iteration, a mixed reward function is generated based on values for the multiplier variables generated in the preceding iteration, and estimates of the rewards and the values of constraint reward functions if the actions are chosen based on the policy model generated in the preceding iteration.