View source for Deepmind technologies limited (20240185083). LEARNING DIVERSE SKILLS FOR TASKS USING SEQUENTIAL LATENT VARIABLES FOR ENVIRONMENT DYNAMICS simplified abstract

=LEARNING DIVERSE SKILLS FOR TASKS USING SEQUENTIAL LATENT VARIABLES FOR ENVIRONMENT DYNAMICS=


==Organization Name==

[[:Category:deepmind technologies limited|deepmind technologies limited]]


[[Category:deepmind technologies limited]]

==Inventor(s)==

[[:Category:Steven Stenberg Hansen of London (GB)|Steven Stenberg Hansen of London (GB)]][[Category:Steven Stenberg Hansen of London (GB)]]

[[:Category:Guillaume Desjardins of London (GB)|Guillaume Desjardins of London (GB)]][[Category:Guillaume Desjardins of London (GB)]]

==LEARNING DIVERSE SKILLS FOR TASKS USING SEQUENTIAL LATENT VARIABLES FOR ENVIRONMENT DYNAMICS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240185083 titled 'LEARNING DIVERSE SKILLS FOR TASKS USING SEQUENTIAL LATENT VARIABLES FOR ENVIRONMENT DYNAMICS

==Simplified Explanation==

The patent application discusses methods for controlling agents to achieve goals by following a sequence of local goals and corresponding training methods. The system models environment dynamics by sampling latent variables, which are used to condition an action-selection policy neural network for diverse state exploration and exploratory behavior.

* The innovation involves modeling environment dynamics using latent variables to guide action selection.
* The system encourages exploratory behavior by allowing agents to reach diverse states through a sequence of local goals.
* Training methods efficiently model the sequence of latent variables through a linear and recurrent relationship.
* Avoids the need to learn a state-dependent higher-level policy for selecting latent variables, making training more practical.

Potential Applications:
- Autonomous robots
- Video game AI
- Industrial automation

Problems Solved:
- Encouraging exploratory behavior in agents
- Efficient training of action-selection policies

Benefits:
- Increased diversity in agent behavior
- More efficient training process
- Enhanced adaptability to changing environments

Commercial Applications:
Title: "Enhancing Agent Control Systems for Diverse State Exploration"
This technology can be applied in industries such as robotics, gaming, and automation to improve agent performance and adaptability in dynamic environments.

Prior Art:
No prior art information available at this time.

Frequently Updated Research:
There is ongoing research in the field of reinforcement learning and agent control systems to further enhance the efficiency and effectiveness of training methods.

Questions about Agent Control Systems:

Question 1: How does the system ensure efficient training of action-selection policies?
Answer: The system efficiently models the sequence of latent variables through a linear and recurrent relationship, avoiding the need to learn a state-dependent higher-level policy.

Question 2: What are the potential applications of this technology beyond autonomous robots?
Answer: This technology can also be applied in video game AI and industrial automation for enhanced agent performance and adaptability.


==Original Abstract Submitted==

this specification relates to methods for controlling agents to perform actions according to a goal (or option) comprising a sequence of local goals (or local options) and corresponding methods for training. as discussed herein, environment dynamics may be modelled sequentially by sampling latent variables, each latent variable relating to a local goal and being dependent on a previous latent variable. these latent variables are used to condition an action-selection policy neural network to select actions according to the local goal. this allows the agents to reach more diverse states than would be possible through a fixed latent variable or goal, thereby encouraging exploratory behavior. in addition, specific methods described herein model the sequence of latent variables through a simple linear and recurrent relationship that allows the system to be trained more efficiently. this avoids the need to learn a state-dependent higher level policy for selecting the latent variables which can be difficult to train in practice.



[[Category:G06N3/092]]