Aircraft Control Agent Training Across Multi-Rule Flight Environments
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Solution Overview
Problem
Existing aircraft control algorithms are deterministic and predictable, making them exploitable and less effective in achieving mission objectives, particularly in unmanned aerial vehicles (UAVs).
Innovation Solution
A method for training an agent to control an aircraft using two distinct environments with different rules, where the agent selects actions based on states and updates its strategies based on rewards specific to each environment, allowing for progressive learning of basic flight control and defensive/offensive maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a deterministic algorithm is used to control the aircraft, then the control logic is simple and predictable, but the aircraft control becomes exploitable and less effective in achieving mission objectives
Solution Approach 1:
The patent applies dynamics by transitioning from static deterministic rules to a dynamic reinforcement learning agent that continuously adapts its control strategy based on environmental feedback. The agent updates its policy iteratively through interaction with the environment, enabling the aircraft to exhibit adaptive, unpredictable behavior while maintaining effective mission performance.
Solution Approach 2:
The patent implements parameter changes by modifying the control algorithm from fixed deterministic parameters to learned probabilistic parameters. The reinforcement learning agent learns optimal action probabilities through training, allowing the control behavior to change parameters dynamically based on observed states rather than following predetermined rules.
2Productivity
If a single training environment is used, then the training process is simple and fast, but the agent learns predictable patterns that are exploitable
Solution Approach 1:
The patent applies segmentation by dividing the training process into multiple distinct environments, each with different rules and characteristics. The agent trains separately in each environment (e.g., cooperative environment with friendly aircraft, competitive environment with hostile aircraft), learning diverse strategies that prevent overfitting to a single scenario and reduce predictability.
Solution Approach 2:
The patent implements universality by designing a multi-environment training framework where the agent learns transferable skills applicable across different operational contexts. The agent develops universal control capabilities that function effectively in various mission scenarios, from cooperative operations to competitive engagements, rather than specializing in a single environment.
3Reliability
If multiple training environments with different rules are used, then the agent learns more robust and less predictable control strategies, but the training complexity and time increase
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple training environments with distinct rules and characteristics before agent training begins. These environments are carefully designed in advance to cover various operational scenarios, allowing the agent to systematically learn robust strategies across different conditions without encountering unexpected complexities during training.
Solution Approach 2:
The patent implements copying by creating virtual simulated environments that replicate real-world aircraft operational conditions. Instead of training directly on complex real aircraft systems, the agent learns in simplified yet representative simulated copies of the target system, reducing training complexity while maintaining transferability to the actual aircraft.
Data Source
AI summary
An example includes a method for training an agent to control an aircraft. The method includes: selecting, by the agent, first actions for the aircraft to perform within a first environment respectively during first time intervals based on first states of the first environment during the first time intervals, updating the agent based on first rewards that correspond respectively to the first states, selecting, by the agent, second actions for the aircraft to perform within a second environment respectively during second time intervals based on second states of the second environment during the second time intervals, and updating the agent based on second rewards that correspond respectively to the second states. At least one first rule of the first environment is different from at least one rule of the second environment.