Reinforcement Learning from Delayed Observations via World Models: Addressing Observation Delays in RL Environments

核心概念

World models can effectively handle observation delays in partially observable environments, improving RL performance by up to 30%.

要約

• The paper addresses observation delays in RL settings due to physical constraints.
• Proposes leveraging world models to reduce delayed POMDPs to DMDPs for improved performance.
• Introduces two strategies: delayed actor and latent state imagination, showing resilience against partial observability.
• Experiment results show the Extended method outperforms other approaches, especially in visual input tasks.

統計

"Experiments suggest that one of our methods can out-perform a naive model-based approach by up to %30."

引用

"In scenarios where timely decision-making is critical and agents cannot afford to wait for updated state observations, RL algorithms must nonetheless find effective control policies subject to delay constraints."
"World models have recently shown significant success in integrating past observations and learning the dynamics of the environment."
"Our methods exhibit greater resilience and one of them improves by approximately 30%."

抽出されたキーインサイト

Reinforcement Learning from Delayed Observations via World Models

by Armin Karamz... 場所 arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12309.pdf

深掘り質問

How can the concept of observation delays be applied beyond reinforcement learning?

Observation delays are not exclusive to reinforcement learning and can impact various fields where real-time decision-making is crucial. One application could be in autonomous vehicles, where delayed sensor data or communication signals could affect driving decisions. In healthcare, delays in receiving patient information or test results could impact treatment decisions. Additionally, in finance, delayed market data could influence trading strategies. By addressing observation delays effectively, these fields can improve decision-making processes and overall performance.

What are potential drawbacks or limitations of relying on world models for handling observation delays?

While world models have shown promise in handling observation delays in reinforcement learning settings, there are some drawbacks and limitations to consider:

Complexity: Implementing world models can add complexity to the system architecture.
Training Data: World models require a significant amount of training data to accurately model the environment dynamics.
Generalization: World models may struggle with generalizing across different environments or scenarios.
Computational Resources: Training and using world models may require substantial computational resources.
Accuracy: The accuracy of predictions made by world models may degrade over time due to cumulative errors.

How might advances in handling observation delays impact real-world applications outside of traditional RL settings?

Advances in handling observation delays can have far-reaching impacts on various real-world applications:

Healthcare: Improved delay-aware systems can enhance patient monitoring and diagnosis accuracy.
Autonomous Vehicles: Reduced latency in sensor data processing can lead to safer and more efficient self-driving cars.
Finance: Real-time analysis with minimal delay can optimize trading strategies and risk management.
Manufacturing: Timely feedback from sensors can enhance production efficiency and quality control processes.
Telecommunications: Minimizing communication latency improves network performance for better user experiences.

By addressing observation delays effectively across diverse domains, advancements in this area have the potential to revolutionize how systems operate efficiently under varying degrees of observability constraints while making critical decisions swiftly based on delayed information streams."