Real-Time Reinforcement Learning Architecture for Delay-Aware Control
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Solution Overview
Problem
Reinforcement learning models struggle in real-world environments due to issues like slow data collection, partial observability, noisy sensors, safety concerns, and time delays between events, leading to poor performance compared to simulated environments.
Innovation Solution
The proposed reinforcement learning architecture addresses these challenges by reducing time delays through joint vector data collection and suspending the reinforcement learning agent for a defined period before transitioning to an active state, ensuring consistent time intervals for decision-making and adapting to various devices and configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning models are applied to real-world environments with physical robotics and devices, then the system can operate in real-time physical settings, but the performance deteriorates due to time delays and uncertainties
Solution Approach 1:
The patent creates a simulated environment that copies the structure and dynamics of the real-world physical environment. The simulation includes virtual representations of robots, sensors, and physical objects that replicate real-world interactions. This allows the reinforcement learning agent to train in a faithful copy of the real environment without suffering from real-world time delays and uncertainties, while maintaining adaptability to real-world operation.
2Speed
If reinforcement learning operates in real-time with physical devices, then real-world interaction is achieved, but time delays between events disassociate causation between actions and observations
Solution Approach 1:
The patent implements a feedback mechanism where the simulated environment provides immediate feedback to the reinforcement learning agent about the outcomes of actions. The simulation environment processes actions and returns observations and rewards without the time delays present in physical systems. This maintains the causal relationship between actions and observations by ensuring that feedback about action outcomes is received promptly, allowing the agent to learn accurate cause-effect relationships.
3Reliability
If reinforcement learning uses traditional data collection methods in physical environments, then real-world data is gathered, but the slow rate of data collection limits learning speed
Solution Approach 1:
The patent creates a simulated environment that copies the real-world physical system, allowing data to be collected in the simulation rather than directly from physical devices. The simulation can generate data at much higher rates than physical data collection because it doesn't受制于 physical sensor sampling rates, communication delays, and safety constraints. The simulated data maintains the statistical properties and dynamics of real-world data, ensuring data quality while dramatically increasing the data collection rate for faster learning.
4Adaptability or versatility
If reinforcement learning systems operate with external robotic systems, then real-world tasks can be performed, but responsiveness to action commands and stimulus is reduced
Solution Approach 1:
The patent trains the reinforcement learning agent in a simulated environment before deploying to real-world robotic systems. The agent learns policies and response patterns in advance through extensive simulation training. This preliminary training in simulation allows the agent to develop refined action selection and response strategies that can be quickly applied when deployed to real robots, improving responsiveness because the heavy learning work was completed beforehand in the faster simulation environment.
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AI summary
A reinforcement learning architecture for facilitating reinforcement learning in connection with operation of an external real-time system that includes a plurality of devices operating in a real-world environment. The reinforcement learning architecture includes a plurality of communicators, a task manager, and a reinforcement learning agent that interact with each other to effectuate a policy for achieving a defined objective in the real-world environment. Each of the communicators receives sensory data from a corresponding device and the task manager generates a joint state vector based on the sensory data. The reinforcement learning agent generates, based on the joint state vector, a joint action vector, which the task manager parses into a plurality of actuation commands. The communicators transmit the actuation commands to the plurality of devices in the real-world environment.