Deepmind technologies limited (20240185084). MULTI-OBJECTIVE REINFORCEMENT LEARNING USING WEIGHTED POLICY PROJECTION simplified abstract
Contents
MULTI-OBJECTIVE REINFORCEMENT LEARNING USING WEIGHTED POLICY PROJECTION
Organization Name
Inventor(s)
Abbas Abdolmaleki of London (GB)
Sandy Han Huang of London (GB)
Martin Riedmiller of Balgheim (DE)
MULTI-OBJECTIVE REINFORCEMENT LEARNING USING WEIGHTED POLICY PROJECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185084 titled 'MULTI-OBJECTIVE REINFORCEMENT LEARNING USING WEIGHTED POLICY PROJECTION
Simplified Explanation
Simplified Explanation: The patent application describes computer systems and methods for training a neural network to select actions for an agent to perform tasks, optimizing multiple objectives including staying close to a teacher's behavioral policy.
- Neural network trained to select actions for agent control
- Optimization of multiple objectives, including teacher's behavioral policy
- Mechanism for defining trade-off between objectives
Potential Applications: 1. Robotics: Implementing efficient action selection policies for robotic agents. 2. Gaming: Enhancing AI decision-making in video games. 3. Autonomous Vehicles: Improving decision-making processes for self-driving cars.
Problems Solved: 1. Enhancing agent performance through optimized action selection. 2. Balancing multiple objectives in decision-making processes. 3. Learning from predetermined datasets to improve offline training.
Benefits: 1. Improved task performance for agents. 2. Enhanced decision-making capabilities. 3. Efficient optimization of multiple objectives.
Commercial Applications: Optimizing action selection policies can benefit industries such as robotics, gaming, and autonomous vehicles by improving agent performance and decision-making processes.
Prior Art: Research in reinforcement learning and neural network training for action selection policies may be relevant to this technology.
Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms and neural network training techniques for action selection policies.
Questions about the Technology: Question 1: How does this technology compare to traditional decision-making algorithms? This technology utilizes neural networks to optimize action selection policies, providing more flexibility and adaptability compared to traditional algorithms.
Question 2: What are the potential limitations of using a teacher's behavioral policy as a reference for training the neural network? While using a teacher's behavioral policy can provide a benchmark for training, it may also limit the network's ability to explore new strategies independently.
Original Abstract Submitted
computer implemented systems and methods for training an action selection policy neural network to select actions to be performed by an agent to control the agent to perform a task. the techniques are able to optimize multiple objectives one of which may be to stay close to a behavioral policy of a teacher. the behavioral policy of the teacher may be defined by a predetermined dataset of behaviors and the systems and methods may then learn offline. the described techniques provide a mechanism for explicitly defining a trade-off between the multiple objectives.
