18272956. Methods And Apparatus For Implementing Reinforcement Learning simplified abstract (Telefonaktiebolaget LM Ericsson (publ))

Methods And Apparatus For Implementing Reinforcement Learning

Organization Name

Telefonaktiebolaget LM Ericsson (publ)

Inventor(s)

Alexandros Nikou of Danderyd (SE)

Anusha Pradeep Mujumdar of Bangalore (IN)

Methods And Apparatus For Implementing Reinforcement Learning - A simplified explanation of the abstract

This abstract first appeared for US patent application 18272956 titled 'Methods And Apparatus For Implementing Reinforcement Learning

The patent application describes methods and apparatus for implementing reinforcement learning (RL) in a node that instructs actions in an environment based on a policy generated by a RL agent.

• The method involves obtaining an intent specifying criteria for the environment, determining a Companion Markov Decision Process (CMDP) encoding environment states, and generating a finite state automaton representing the intent.
• A product of CMDP output states and logic states is computed to select actions from suggested actions in the policy.
• The innovation involves combining CMDP output states and logic states to guide action selection in RL implementation.

Potential Applications: - Autonomous vehicles - Robotics - Industrial automation

Problems Solved: - Efficient action selection in RL environments - Improved decision-making processes

Benefits: - Enhanced performance in complex environments - Optimal utilization of resources

Commercial Applications: Title: "Enhancing Decision-Making in Autonomous Systems" This technology can be used in autonomous vehicles, robotics, and industrial automation to improve decision-making processes, leading to increased efficiency and performance in various applications.

Questions about Reinforcement Learning Implementation: 1. How does the method of combining CMDP output states and logic states improve action selection in RL environments? 2. What are the key advantages of using intent-based decision-making in RL systems?

Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms and applications to enhance decision-making processes in various industries.

Original Abstract Submitted

Methods and apparatus for implementing reinforcement learning (RL) are provided. A method of operation for a node implementing RL, wherein the node instructs actions in an environment in accordance with a policy generated by a RL agent, wherein the RL agent models the environment and encodes a state of the environment using a set of features, comprises obtaining an intent, wherein the intent specifies one or more criteria to be satisfied by the environment. The method further comprises determining a Companion Markov Decision Process (CMDP) that encodes states of the environment using a subset of the set of features used by the RL agent. The method further comprises generating a finite state automaton that represents the intent as a series of logic states, and computing a product of CMDP output states and logic states, wherein the product contains all of the potential combinations of a CMDP output state and a logic state. The method further comprises selecting an action to be performed on the environment from one or more suggested actions obtained from the policy, the selection being based on the product of CMDP output states and logic state.