Deepmind technologies limited (20240135190). LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING simplified abstract
Contents
- 1 LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Training Target Action Selection Policy
- 1.13 Original Abstract Submitted
LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING
Organization Name
Inventor(s)
Benjamin Van Roy of Stanford CA (US)
Rahul Anant Jain of Malibu CA (US)
Botao Hao of Redwood City CA (US)
LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135190 titled 'LEVERAGING OFFLINE TRAINING DATA AND AGENT COMPETENCY MEASURES TO IMPROVE ONLINE LEARNING
Simplified Explanation
The patent application describes methods, systems, and apparatus for training a target action selection policy to control a target agent interacting with an environment. This involves obtaining offline training data from a baseline agent, generating online training data from the target agent, and training the target action selection policy using both sets of data.
Key Features and Innovation
- Training a target action selection policy based on offline and online training data.
- Conditioning the training on the competency of the baseline agent.
- Characterizing interaction of agents with the environment through data collection and policy training.
Potential Applications
This technology can be applied in various fields such as robotics, autonomous systems, and artificial intelligence for enhancing agent performance and decision-making processes.
Problems Solved
- Improving the decision-making capabilities of agents.
- Enhancing the efficiency and effectiveness of agent interactions with the environment.
- Facilitating the training of target action selection policies based on real-world data.
Benefits
- Enhanced performance of target agents.
- Improved decision-making processes.
- Efficient training methods based on real-world data.
Commercial Applications
- Autonomous vehicles for better navigation and decision-making.
- Robotics for more precise and efficient operations.
- Gaming industry for creating more realistic and intelligent virtual agents.
Prior Art
There may be existing technologies related to training action selection policies for agents, but the specific conditioning on the competency of the baseline agent may be a novel aspect of this innovation.
Frequently Updated Research
There may be ongoing research in the fields of reinforcement learning, machine learning, and artificial intelligence that could be relevant to the development and application of this technology.
Questions about Training Target Action Selection Policy
Question 1
How does the conditioning on the competency of the baseline agent impact the training of the target action selection policy?
Question 2
What are the potential challenges in implementing this technology in real-world applications?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a target action selection policy to control a target agent interacting with an environment. in one aspect, a method comprises: obtaining a set of offline training data, wherein the offline training data characterizes interaction of a baseline agent with an environment as the baseline agent performs actions selected in accordance with a baseline action selection policy; generating a set of online training data that characterizes interaction of the target agent with the environment as the target agent performs actions selected in accordance with the target action selection policy; and training the target action selection policy on both: (i) the offline training data, and (ii) the online training data, wherein the training of the target action selection policy on the offline training data is conditioned on a measure of competency of the baseline agent.
