Self-Model-Free Learning versus Learning with External Rewards in Information-Constrained Environments
Self-Model-Free Learning vs. Learning with External Rewards in Information-Constrained Environments: A New Reinforcement Learning Framework
In recent years, with the development of networks and artificial intelligence systems, networked learning mechanisms face significant security challenges. In the domain of reinforcement learning (RL), the loss of reward signals, packet drops, and intentional network attacks have become critical obstacles affecting the performance of learning systems. Addressing this issue, Prachi Pratyusha Sahoo (IEEE Student Member) and Kyriakos G. Vamvoudakis (IEEE Senior Member) from the Georgia Institute of Technology proposed a novel reinforcement learning framework relying on internal reward signals, termed “Self-Model-Free RL.” Published in the December 2024 edition of IEEE Transactions on Artificial Intelligence, this study showcases how to design reliable strategy-generation methods when reward signals are lost.
Background and Research Motivation
Intelligent Cyber-Physical Systems (CPS), widely applied in areas like autonomous driving, therapeutic and entertainment robotics, and smart grids, are known for their high autonomy, adaptability, and self-healing capabilities. However, their complex communication topology and information-sharing mechanisms render them susceptible to malicious attacks, such as packet losses, wireless interference, and sensor spoofing attacks. In adversarial environments where external reward signals drastically reduce or are entirely lost, traditional reinforcement learning algorithms face significant challenges in optimizing control strategies.
Existing research has proposed solutions for handling reward signal degradation, packet losses, and malicious attacks, including online Q-learning, Kalman filter-based signal estimation, and neural network-based signal compensation. However, the literature lacks synchronous internal compensation methods with theoretical guarantees. This research addresses this gap by introducing an adaptive mechanism that provides internal compensation when reward signals are lost and applies a trade-off mechanism when partial reward signals are available to ensure stability and optimality in strategy generation.
Research Methodology and Workflow
The authors designed and validated a reinforcement learning framework based on a “goal network” and proposed two core compensation mechanisms: pure internal reward mechanism and internal-external reward trade-off mechanism.
1. Framework Overview
The framework’s core objective is to compensate for lost reward signals under adversarial disturbances and information loss, thereby generating reliable control strategies. The key methods include: - Self-Model-Free RL Compensation Mechanism: Construct a goal network to simulate and compensate for lost reward signals. - Goal Network Design and Training: Design a network that generates substitute reward signals when the original signals (e.g., due to packet drop) are entirely lost. - Trade-off Reward Mechanism: For scenarios where partial reward signals are available, employ a hybrid strategy that uses real rewards when available and switches to estimated rewards when signals are lost.
2. Experiment Design and Data Handling
Experiments followed these steps: - Linear Time-Invariant System Modeling: Test framework performance with a basic example (e.g., spring-mass-damper system) and a complex case (e.g., F-16 fighter jet flight control system). - Neural Network Representation and Signal Reconstruction: Use goal networks to estimate lost signals while employing actor–critic architectures to incrementally optimize strategy weights. - Dynamic Learning Weight Adjustment: Tune the network models using error feedback mechanisms to ensure convergence and stability in control inputs.
3. Algorithm Design
- Pure Internal Reward Mechanism: The system relies entirely on internally constructed reward signals for strategy evaluation. The goal network dynamically adjusts weights to minimize error between internal rewards and actual environmental rewards.
- Trade-off Reward Mechanism: By recording the availability of reward signals (using a Boolean variable, p(t)), the system utilizes true rewards when accessible and switches to estimated signals otherwise. Hybrid signals dynamically balance their impact during training.
Research Results and Analysis
1. Theoretical Guarantees
The authors demonstrated the framework’s convergence using Lyapunov stability theory: - Under the pure internal reward mechanism, both goal network and strategy weights exhibit exponential stability. - In the trade-off reward mechanism, despite partial reward signal loss, strategy generation still achieves sub-optimal levels and continuously improves toward optimal solutions.
2. Simulation Experiments
Experiments validated the framework using a spring-mass-damper system and an F-16 fighter jet system: - Pure Internal Reward Mechanism: Reward loss resulted in higher integral costs, but the system could gradually stabilize. During early training phases, the goal network may lead to significant strategy deviations due to incomplete signal estimation, but results improved substantially with iterations. - Trade-off Reward Mechanism: Owing to dynamic balancing of hybrid signals, this method consistently ensured sub-optimal decision-making under varying levels of reward signal availability, effectively stabilizing the systems.
3. Data and Performance Analysis
Experimental results show that cumulative integral costs during training are positively correlated with the extent of information loss: - Scenarios with no reward signal access yielded the highest integral costs under the pure internal reward mechanism. - The trade-off reward mechanism’s performance depends significantly on the extent of available signals. While estimated rewards may overestimate true rewards and lead to higher cumulative costs, the method successfully guided the spring-mass-damper system and F-16 jet to stability. - Scenarios with complete external signal access (baseline Q-learning) yielded the most optimal performance, serving as the ideal reference.
4. Model Limitations
Although the model demonstrates apparent advantages, its optimality heavily depends on the accuracy of signal compensation. Extending the study to nonlinear, time-variant systems or distributed control scenarios will be a valuable future direction.
Academic Significance and Practical Value
1. Academic Contribution
The study provides a novel framework for stabilizing strategy generation in RL under adverse environments, with unique innovations compared to existing methods: - Synchronous Internal Compensation: The goal network can compensate for signal loss in real time and provide convergence guarantees without requiring model assumptions. - Balance Mechanism for Stability: The dynamic adjustment between internal and external rewards ensures robustness under complex environmental settings.
2. Practical Value
This research holds significant value for safer and more efficient deployment of networked CPS. Key applications include: - Enhancing the robustness of systems like autonomous vehicles, robots, and smart grids in adversarial environments. - Tackling information loss problems caused by network attacks, offering theoretical guarantees for industrial intelligent system security.
Conclusion and Future Directions
This research proposed a “Self-Model-Free RL” framework offering innovative solutions for reinforcement learning challenges in information-constrained environments. The authors recommend future studies to optimize computational efficiency, extend applicability to higher-order nonlinear systems, and develop collaborative learning mechanisms in distributed environments.
This research marks a significant step forward in RL and security studies, providing scientific and engineering assurances for the robustness and safety of machine learning models.