How can temporal abstraction improve the efficiency of reinforcement learning algorithms?

Temporal abstraction enables RL agents to handle complex tasks more efficiently by focusing on higher-level actions that encapsulate a sequence of lower-level actions. For example, An automated threat hunting system can have a high level abstraction of network scanning which involves low level actions like monitoring, analyzing network packets and inspecting system logs.

Think of temporal abstraction in AI like planning a road trip. Instead of focusing on every turn and street along the way, you focus on reaching key milestones: getting to the highway, stopping for lunch, and arriving at your destination. For AI, this means breaking big goals into manageable steps. Instead of deciding on every small action, the AI focuses on these larger, important actions to achieve broader objectives. This approach makes learning faster and more efficient because the AI can concentrate on significant actions rather than every tiny detail. So, temporal abstraction helps AI learn and act more effectively by thinking in bigger, useful steps.

Temporal abstraction in reinforcement learning is synonymous to pixel resolution in an image - it is simply a method of breaking the problem down into distinct steps with which an agent can learn. Try to remove your own bias in temporal abstraction from the problem. Experiment with different time steps and, where possible, state discretization to see what the robot learns at most efficiently. Some temporal abstracts may perform better than others in certain scenarios, or you may find that one temporal abstraction gives the best rewards in all scenarios. It may surprise you which temporal resolution actually informs the best rewards based on how you abstract the problem.

L'abstraction temporelle améliore l'efficacité de l'apprentissage par renforcement en : Réduisant la complexité du problème Permettant des actions de plus haut niveau Accélérant l'exploration de l'espace d'états Facilitant le transfert de connaissances entre taches Améliorant la stabilité de l'apprentissage sur le long terme

One of the main reasons why temporal abstraction improves RL efficiency is that it allows the agents to learn complicated tasks much faster by breaking them down into simpler subtasks, which accelerates learning and enhances performance.

With temporal abstraction, RL agent choose the high level actions first and focus on corresponding low level actions instead of handling all low level actions in the action space. This simplify the decision making and improve the efficiency of RL agent.

Formally defined by the concept of options (D. Precup - Temporal Abstraction in Reinforcement Learning (2000, PhD Thesis)) or viewed as Semi-Markov Decision Process (R. E. Parr - Hierarchical Control and Learning for Markov Decision Processes (1998, PhD Thesis))

Hierarchical Abstract Machines (HAMs) provide a way to specify high-level actions and their transitions, allowing for a clear and structured representation of hierarchical policies using state machines. These state machines encapsulate sequences of low-level actions, making it easier to manage complex tasks.

I once worked on an RL-based robot navigation system that initially struggled with slow learning due to micromanaging every step. By introducing temporal abstraction, I shifted the focus to high-level actions like “Go to the door” or “Move to the next room,” speeding up the learning process and enabling better generalization across environments. Temporal abstraction boosts RL efficiency by reducing decision-making granularity, allowing agents to focus on broader objectives. Methods like hierarchical RL, the options framework, and skill discovery decompose tasks into subtasks, improving scalability. However, challenges include designing effective hierarchies and handling non-stationary environments.

Temporal abstraction in RL can be used for threat hunting in cyber security by separating out high level actions such as network scanning, analyzing suspicious activity and isolating infected devices into low level actions such as checking network traffic, inspecting system logs, running anti-malware tools and alerting security system.

You see a lot of applications of temporal abstractions in stock trading. A stock-trading bot that trades through reinforcement learning will not make the same trades on 1-minute intervals as it will on 1-day or 1-month intervals. Stock movements are different at different time resolutions and the state space changes as a result. A trading bot trained to maximize portfolio value by trading on 1-minute intervals may not maximize the portfolio value if it is deployed in an environment where it can only make trades once per month. Keep in mind that the temporal resolution of your training environment must be the same as your deployed environment.

Scalability is one of the challenge as RL system need to scale over big data. One solution is to reduce the state space and focusing only on key features of data when designing state space.