Symbolic Reinforcement Learning on Visual Inputs With Safe Action Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing reinforcement learning algorithms for safe control of physical systems, such as self-driving cars, face challenges in providing formal and interpretable safety guarantees, especially when processing high-dimensional visual inputs from sensors, as they require manual mapping of visual inputs into symbolic state spaces, which is time-consuming and often intractable, and do not adequately address safety constraints during training.
Innovation Solution
A deep reinforcement learning algorithm that uses visual templates to map visual inputs into a symbolic state space, enabling the specification and enforcement of safety constraints by learning a template-based mapping to create a partial symbolic representation of the system's state, allowing domain experts to define safe and unsafe actions without relying on an oracle or complete symbolic representation of the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual mapping of visual inputs into symbolic state spaces is used, then formal safety guarantees can be provided, but the process is time-consuming and often intractable
Solution Approach 1:
The patent replaces manual mechanical mapping processes with automated deep learning-based visual template matching systems. The system automatically extracts symbolic state information from visual inputs using trained neural networks that perform template matching, substituting the manual feature extraction and state space construction process with an automated computational approach that maintains formal safety guarantees while dramatically reducing time requirements.
Solution Approach 2:
The patent introduces visual templates as an intermediary between raw visual inputs and symbolic state representations. These templates serve as a bridge that enables automated extraction of meaningful state variables from complex visual data, allowing the system to construct symbolic state spaces automatically without manual intervention while preserving the formal properties needed for safety verification.
2Reliability
If complete symbolic representation of the environment is required, then formal verification can be performed, but it increases system complexity and requires oracle functions that are infeasible to implement
Solution Approach 1:
The patent applies partial action by constructing a partial symbolic state space that includes only the variables necessary for safety verification rather than attempting to represent the complete environment state. The visual template matching system extracts and tracks only those objects and properties that are relevant to safety constraints, avoiding the intractable task of fully specifying the entire environment while still enabling formal verification of safety properties.
Solution Approach 2:
The patent extracts only the essential safety-critical variables from visual inputs using template matching, rather than attempting to model the complete environment. The system identifies and extracts specific objects, their positions, and relevant properties that are necessary for safety verification, discarding extraneous information that would increase complexity without contributing to safety guarantees.
3Productivity
If reinforcement learning trains over complete state spaces, then optimal policies can be learned, but safety constraints cannot be enforced during training
Solution Approach 1:
The patent performs preliminary action by pre-defining safety constraints and visual templates before the reinforcement learning training begins. The safety specifications are established a priori, and the visual template matching system is pre-configured to extract the necessary state variables. During training, these pre-established safety constraints are automatically enforced by checking whether learned policies violate the predefined safety rules, enabling both effective learning and safety enforcement without requiring complete state space exploration.
Data Source
AI summary
A method for training control software to reinforce safety constraints using visual inputs includes performing template matching for each object in an image of a reinforcement learning (RL) agent's action space using a visual template for each object wherein each object in the RL agent's action space is detected, mapping each detected object to a set of planar coordinates for each object in the RL agent's action space, determining a set of safe actions for the RL agent by applying a safety specification for the RL agent's action space to the set of variables for coordinates for each object in the RL agent's action space, outputting the set of safe actions to the RL agent for a current state of a RL procedure, and preventing the RL agent from executing an action that is unsafe, before the RL agent takes an action.