betekintés - Reinforcement learning control - # Transformer-based control policy learning

Decision Transformer as a Versatile Foundation Model for Controlling Partially Observable Nonlinear Dynamical Systems

Q: How can the DT architecture be further refined to learn efficiently from highly non-stationary demonstration policies, such as finite-horizon linear-quadratic Gaussian (LQG) controllers

To enhance the Decision Transformer (DT) architecture's ability to efficiently learn from highly non-stationary demonstration policies, such as finite-horizon linear-quadratic Gaussian (LQG) controllers, several refinements can be considered: Temporal Segmentation: Implement a mechanism to segment trajectories based on the absolute time steps rather than relative temporal information within each segment. This would allow DT to capture the temporal dependencies present in non-stationary policies like finite-horizon LQG controllers. Context Length Adaptation: Introduce adaptive context length mechanisms that can adjust based on the complexity and non-stationarity of the demonstration policies. This would enable DT to effectively capture the relevant historical information required for learning from non-stationary policies. Dynamic Prompting: Develop dynamic prompting strategies that can guide DT to focus on specific segments of the demonstration trajectories that are more informative for learning from non-stationary policies. This adaptive prompting can help DT extract key insights from the demonstrations efficiently. Incorporating State Estimation: Integrate state estimation modules within the DT architecture to handle the intricate in-loop state estimation required by non-stationary policies like finite-horizon LQG controllers. This would enable DT to effectively utilize the historical information for learning and decision-making. By incorporating these refinements, the DT architecture can be better equipped to learn efficiently from highly non-stationary demonstration policies, enhancing its performance in control tasks with complex dynamics and varying temporal characteristics.

Q: What are the theoretical properties and performance guarantees of DT in the context of partially observable control tasks, especially regarding stability, robustness, and optimality

In the context of partially observable control tasks, the Decision Transformer (DT) architecture exhibits several theoretical properties and performance guarantees related to stability, robustness, and optimality: Stability: DT's autoregressive prediction of optimal actions based on past observations, actions, and rewards can lead to stable control policies. By leveraging the Transformer's exceptional representational capabilities, DT can capture the underlying dynamics of partially observable systems and generate stable control actions even in the presence of noise and uncertainties. Robustness: DT's ability to generalize to new tasks with minimal demonstrations showcases its robustness in adapting to unseen scenarios. The pre-trained language models and low-rank adaptation techniques enable DT to quickly adapt to different system parameters and control objectives, demonstrating robust performance across a diverse set of control tasks. Optimality: Through the efficient compression of historical information into an "approximate information state," DT can make optimal decisions in partially observable settings. By leveraging pre-trained language models and training for control tasks using low-rank adaptation, DT can achieve near-optimal performance levels and surpass expert behavior policies with minimal demonstration data. Overall, DT's theoretical properties and performance guarantees in partially observable control tasks highlight its potential as a foundational controller for general control applications, showcasing stability, robustness, and optimality in diverse and challenging environments.

Q: Given the success of DT in control tasks, how can the insights and techniques be extended to other domains, such as multi-agent coordination, hybrid systems, or safety-critical applications

The success of the Decision Transformer (DT) architecture in control tasks can be extended to other domains, such as multi-agent coordination, hybrid systems, and safety-critical applications, by leveraging its key insights and techniques: Multi-Agent Coordination: DT's ability to capture complex dependencies and generalize to new tasks can be applied to multi-agent coordination scenarios. By training DT on interactions between multiple agents and environments, it can learn effective coordination strategies and adapt to dynamic team dynamics, enhancing overall system performance. Hybrid Systems: DT's autoregressive prediction approach can be utilized in hybrid systems where discrete and continuous dynamics coexist. By incorporating hybrid system models and training DT on hybrid control tasks, it can effectively handle the complexities of mixed-mode systems and optimize control actions in such environments. Safety-Critical Applications: In safety-critical applications, DT's rapid adaptation capabilities and zero-shot generalization can be invaluable. By training DT on safety-critical control tasks and incorporating robustness constraints, it can ensure safe and reliable operation in dynamic and uncertain environments, mitigating risks and optimizing system performance under stringent safety requirements. By extending DT's insights and techniques to these domains, it is possible to enhance decision-making, coordination, and control in complex and challenging scenarios, paving the way for the application of DT as a foundational controller in a wide range of critical applications.

Alapfogalmak

Decision Transformer can effectively learn control policies for partially observable nonlinear dynamical systems, exhibiting remarkable zero-shot generalization and rapid adaptation to new tasks with minimal demonstrations.

Kivonat

The content presents an investigation of the Decision Transformer (DT) architecture as a foundation model for controlling partially observable nonlinear dynamical systems. The key highlights are:

The control problem is formulated as predicting the current optimal action based on a sequence of past observations, actions, and rewards, eliminating the need for a separate state estimator design.
DT is initialized using a pre-trained GPT-2 language model and then trained on self-generated offline control datasets via Low-Rank Adaptation (LoRA).
Comprehensive experiments are conducted on five distinct control tasks, ranging from maneuvering aerospace systems to controlling partial differential equations (PDEs).
In the single-task setting, DT consistently matches or surpasses the performance of unknown expert behavior policies and state-of-the-art offline reinforcement learning methods.
In the multi-task setting with significantly perturbed system dynamics, DT exhibits remarkable zero-shot generalization abilities to new tasks and rapidly surpasses expert performance levels with a minimal amount of demonstration data (e.g., 10 rollout trajectories).
The experimental findings confirm DT's ability to capture parameter-agnostic structures of control tasks and excel in few-shot learning, suggesting its potential as a foundational controller for general control applications.

Összefoglaló testreszabása

Átírás mesterséges intelligenciával

Hivatkozások generálása

Forrás fordítása

Egy másik nyelvre

Gondolattérkép létrehozása

a forrásanyagból

Forrás megtekintése

arxiv.org

Statisztikák

The content does not provide specific numerical data or metrics to support the key claims. However, it presents comparative performance results between DT, expert behavior policies, and state-of-the-art offline reinforcement learning methods across the five control tasks.

Idézetek

The content does not contain any striking quotes that directly support the key logics.

Főbb Kivonatok

Decision Transformer as a Foundation Model for Partially Observable Continuous Control

by Xian... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02407.pdf

Decision Transformer as a Foundation Model for Partially Observable Continuous Control

Mélyebb kérdések

How can the DT architecture be further refined to learn efficiently from highly non-stationary demonstration policies, such as finite-horizon linear-quadratic Gaussian (LQG) controllers

To enhance the Decision Transformer (DT) architecture's ability to efficiently learn from highly non-stationary demonstration policies, such as finite-horizon linear-quadratic Gaussian (LQG) controllers, several refinements can be considered:

Temporal Segmentation: Implement a mechanism to segment trajectories based on the absolute time steps rather than relative temporal information within each segment. This would allow DT to capture the temporal dependencies present in non-stationary policies like finite-horizon LQG controllers.

Context Length Adaptation: Introduce adaptive context length mechanisms that can adjust based on the complexity and non-stationarity of the demonstration policies. This would enable DT to effectively capture the relevant historical information required for learning from non-stationary policies.

Dynamic Prompting: Develop dynamic prompting strategies that can guide DT to focus on specific segments of the demonstration trajectories that are more informative for learning from non-stationary policies. This adaptive prompting can help DT extract key insights from the demonstrations efficiently.

Incorporating State Estimation: Integrate state estimation modules within the DT architecture to handle the intricate in-loop state estimation required by non-stationary policies like finite-horizon LQG controllers. This would enable DT to effectively utilize the historical information for learning and decision-making.

By incorporating these refinements, the DT architecture can be better equipped to learn efficiently from highly non-stationary demonstration policies, enhancing its performance in control tasks with complex dynamics and varying temporal characteristics.

What are the theoretical properties and performance guarantees of DT in the context of partially observable control tasks, especially regarding stability, robustness, and optimality

In the context of partially observable control tasks, the Decision Transformer (DT) architecture exhibits several theoretical properties and performance guarantees related to stability, robustness, and optimality:

Stability: DT's autoregressive prediction of optimal actions based on past observations, actions, and rewards can lead to stable control policies. By leveraging the Transformer's exceptional representational capabilities, DT can capture the underlying dynamics of partially observable systems and generate stable control actions even in the presence of noise and uncertainties.

Robustness: DT's ability to generalize to new tasks with minimal demonstrations showcases its robustness in adapting to unseen scenarios. The pre-trained language models and low-rank adaptation techniques enable DT to quickly adapt to different system parameters and control objectives, demonstrating robust performance across a diverse set of control tasks.

Optimality: Through the efficient compression of historical information into an "approximate information state," DT can make optimal decisions in partially observable settings. By leveraging pre-trained language models and training for control tasks using low-rank adaptation, DT can achieve near-optimal performance levels and surpass expert behavior policies with minimal demonstration data.

Overall, DT's theoretical properties and performance guarantees in partially observable control tasks highlight its potential as a foundational controller for general control applications, showcasing stability, robustness, and optimality in diverse and challenging environments.

Given the success of DT in control tasks, how can the insights and techniques be extended to other domains, such as multi-agent coordination, hybrid systems, or safety-critical applications

The success of the Decision Transformer (DT) architecture in control tasks can be extended to other domains, such as multi-agent coordination, hybrid systems, and safety-critical applications, by leveraging its key insights and techniques:

Multi-Agent Coordination: DT's ability to capture complex dependencies and generalize to new tasks can be applied to multi-agent coordination scenarios. By training DT on interactions between multiple agents and environments, it can learn effective coordination strategies and adapt to dynamic team dynamics, enhancing overall system performance.

Hybrid Systems: DT's autoregressive prediction approach can be utilized in hybrid systems where discrete and continuous dynamics coexist. By incorporating hybrid system models and training DT on hybrid control tasks, it can effectively handle the complexities of mixed-mode systems and optimize control actions in such environments.

Safety-Critical Applications: In safety-critical applications, DT's rapid adaptation capabilities and zero-shot generalization can be invaluable. By training DT on safety-critical control tasks and incorporating robustness constraints, it can ensure safe and reliable operation in dynamic and uncertain environments, mitigating risks and optimizing system performance under stringent safety requirements.

By extending DT's insights and techniques to these domains, it is possible to enhance decision-making, coordination, and control in complex and challenging scenarios, paving the way for the application of DT as a foundational controller in a wide range of critical applications.