Reinforcement Learning Agent Intent Encoding via CMDP and Automata
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Solution Overview
Problem
Reinforcement Learning (RL) agents struggle to effectively optimize for dynamic intents with complex logical constraints, as they are typically limited to reward maximization and require significant expert effort for reward engineering, making them ineffective in handling complex criteria and logical constraints.
Innovation Solution
The implementation of a method that uses a Companion Markov Decision Process (CMDP) and finite state automaton to encode and represent intents as logic states, allowing the RL agent to select actions based on the product of CMDP output states and logic states, thereby improving reliability and flexibility in decision-making and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RL agents use reward maximization for decision-making, then they can optimize for a given criterion, but they cannot effectively handle complex logical constraints and dynamic intents
Solution Approach 1:
The patent introduces an intermediary component that translates complex logical constraints and dynamic intents into reward function specifications. This intermediary layer enables the RL agent to handle sophisticated requirements by converting them into the reward maximization framework that the agent natively understands, thus resolving the contradiction between handling complex constraints and maintaining adaptability
Solution Approach 2:
The patent dynamically adjusts reward function parameters based on changing intents and constraints. By modifying the reward function's parameters rather than its fundamental structure, the system can adapt to different criteria and dynamic requirements while maintaining the core RL decision-making mechanism, thus achieving both reliability for specific intents and versatility for different scenarios
2Reliability
If RL agents are trained for a specific criterion, then they can optimize for that criterion, but they cannot be directly utilized for different criteria
Solution Approach 1:
The patent creates a universal RL agent framework that can handle multiple different criteria through a single deployment. The agent maintains its trained optimization capabilities while the surrounding system adapts the reward function parameters to match different use cases, eliminating the need for retraining and enabling easy deployment across diverse scenarios
3Adaptability or versatility
If Reward Machines are used for task specification, then they can handle high-level task decomposition, but they require significant expert effort and knowledge for each new environment
Solution Approach 1:
The patent enables the system to automatically generate and configure reward functions for new environments without requiring expert manual intervention. The intermediary component self-adapts to new environments by learning the task structure and automatically formulating appropriate reward specifications, thus reducing expert effort while maintaining the ability to handle complex tasks
Data Source
AI summary
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.