Weakly Supervised Reinforcement Learning Agent
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Solution Overview
Problem
Reinforcement Learning (RL) agents face challenges such as low performance until they acquire enough experience, difficulty in learning with large state and action spaces, and changing action availability over time, which affects exploration and decision-making efficiency.
Innovation Solution
The implementation of Weakly Supervised Reinforcement Learning (WSRL) that utilizes predetermined domain knowledge through programmable knowledge functions, such as constraining and guide functions, to guide the agent's decisions and exploration, improving initial performance and learning speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reinforcement learning is used without domain knowledge, then the system can operate autonomously, but the agent's performance is low until it acquires enough experience
Solution Approach 1:
The patent applies preliminary action by incorporating domain knowledge functions (constraining and guide functions) before the RL agent begins learning. These functions pre-establish constraints and guidance based on expert knowledge, allowing the agent to start with informed decision-making rather than random exploration. This resolves the contradiction by providing reliable performance from the outset while reducing the time needed to acquire sufficient experience.
Solution Approach 2:
The patent introduces domain knowledge functions as an intermediary between the RL agent and the environment. These functions act as mediators that translate expert knowledge into actionable constraints and guidance, enabling the agent to leverage pre-existing knowledge without requiring extensive trial-and-error learning. This intermediary mechanism improves initial performance while accelerating the learning process.
2Adaptability or versatility
If the agent explores the large state and action spaces without guidance, then it can discover all possible actions, but it is difficult to learn what action is correct for each different state
Solution Approach 1:
The patent applies local quality by implementing state-dependent constraining and guide functions that provide localized guidance for specific states or regions of the state space. Rather than applying uniform constraints across all states, the functions adapt their behavior based on the current state, making the learning process more manageable. This allows the agent to focus exploration on relevant actions for each state while reducing overall learning complexity.
Solution Approach 2:
The patent uses preliminary action by pre-defining constraining and guide functions that encode domain knowledge about appropriate actions for different states. These functions are established before learning begins, providing a framework that guides the agent through the large state and action spaces. This preliminary structure reduces learning complexity while maintaining the ability to discover appropriate actions across the full state space.
3Ease of operation
If the RL agent operates without domain knowledge constraints, then it has full freedom to take any action, but it may take incorrect actions and break itself in the real world
Solution Approach 1:
The patent applies preliminary anti-action by implementing constraining functions that prevent the agent from taking harmful or incorrect actions before it can learn their consequences. These functions encode safety constraints and domain knowledge that block potentially damaging actions, allowing the agent to explore freely within safe boundaries. This resolves the contradiction by maintaining action freedom while ensuring reliability and preventing self-harm.
Solution Approach 2:
The patent introduces domain knowledge constraints as an intermediary layer between the agent's action selection and the actual execution of actions. This intermediary filters and guides actions through safety checks and domain knowledge validation, ensuring that even though the agent has freedom to consider various actions, only safe and appropriate actions are executed. This maintains both ease of operation and reliability.
Data Source
AI summary
A method for reinforcement machine learning uses a reinforcement learning system that has an environment and an agent. The agent has a policy providing a mapping between states of the environment and actions. The method includes: determining a current state of the environment; determining, using the policy, a current policy output based on the current state; determining, using a knowledge function, a current knowledge function output based on the current state; determining an action based on the current policy output and the current knowledge function output; applying the action to the environment resulting in updating the current state and determining a reward; and updating the policy based on at least one of the current state and the reward.