Navigation State Space Abstraction for Autonomous Agents
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Solution Overview
Problem
In complex environments, autonomous devices face challenges in navigation due to the large and high-dimensional state space, which increases training duration and complexity, especially when multiple agents interact and have partial observability, leading to inefficient reinforcement learning and communication protocols.
Innovation Solution
The method involves generating state information about an environment, receiving messages from other devices through emergent communication, abstracting the state space using an abstractor module, and employing a reinforced learning module for navigation, allowing for adaptive abstraction and efficient communication protocol learning to reduce the state space complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is applied for navigation in complex environments with multiple agents, then navigation capability is improved, but training duration and complexity increase significantly
Solution Approach 1:
The patent segments the large state space into smaller, manageable abstract states by identifying common patterns and grouping similar configurations together. This segmentation reduces the effective state space that reinforcement learning must explore, thereby decreasing training duration while preserving navigation capability.
Solution Approach 2:
The patent transforms the original high-dimensional state parameters into abstracted state representations that capture essential features while reducing dimensionality. This parameter transformation maintains the information necessary for navigation decisions while making the state space tractable for reinforcement learning training.
2Device complexity
If the state space is reduced through abstraction, then training complexity decreases, but navigation accuracy may be compromised
Solution Approach 1:
The patent applies different levels of abstraction to different regions of the state space based on their importance and variability. Critical state features that directly impact navigation accuracy are preserved with higher fidelity, while less critical features are abstracted more aggressively, maintaining overall accuracy while reducing complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the reinforcement learning agent learns to interpret abstracted states and adjusts its policy based on the outcomes. This feedback loop ensures that even with abstracted state representations, the agent can achieve accurate navigation by learning the mapping between abstract states and optimal actions.
3Productivity
If multiple agents communicate in complex environments, then collaborative navigation improves, but communication protocol complexity increases
Solution Approach 1:
The patent develops a universal communication protocol that can handle multiple types of interactions and information exchanges between agents through a standardized message format. This universal protocol simplifies communication by providing a single interface for diverse collaborative tasks, reducing protocol complexity while maintaining collaborative efficiency.
Data Source
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AI summary
Methods and apparatuses for generating information for navigation in an environment are disclosed. The method comprises generating, by a device, state information about a part of the environment where the device is positioned, receiving, by the device from at least one other device, messages comprising state information about a part of the environment where the at least one other device is positioned, the meaning of the messages being learned based on emergent communication, abstracting the state space for the environment by an abstractor module based on the generated state information and the received state information messages to provide an abstracted state space, and generating information for the device by a reinforced learning module for navigation in the environment based on the abstracted state space and further state information generated by the device and received from the at least one other device.