Reinforcement Learning Task

A Reinforcement Learning Task is an online reward-maximization task that requires the use of a reinforcement learning algorithm (which involves an agent learning to make decisions through trial and error, aiming to maximize cumulative rewards over time by interacting with a dynamic environment).

• Context:
  • It can (often) involves the challenge of the Exploration/Exploitation Tradeoff, requiring the agent to balance between exploring the environment to find new strategies and exploiting known strategies for maximum reward.
  • It can range from being a Discreate-Space Reinforcement Learning Task to being a Continuous-Space Reinforcement Learning Task.
  • …
• Example(s):
  • an RL-based Autonomous Helicopter Flight Task, as presented in the paper "Robust Deep Reinforcement Learning for Quadcopter Control".
  • a RL-based Robot Control Task, as detailed in "Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control".
  • a RL-based Game Playing Task, ...
  • an RL-based Autonomous System Task, ...
  • an RL-based Adaptive User Interface Task, ...
  • a RL-based Dynamic Item Recommendation Task, ...
  • a RL-based Real-Time Traffic Light Control Task, ...
  • a RL-based Personalized Healthcare Decision Support Task, ...
  • an RL-based Adaptive Energy Management Task, ...
  • an RL-based LLM Model Finetuning Task, (using RLHF).
  • Reward Shaping Task.
  • …
• Counter-Example(s):
  • A Linear Regression task, where the goal is to fit a linear model to a dataset without any interactive decision-making process.
  • A Clustering Task, which involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups, without the use of rewards or interactive environments.
• See: Model-Based Reinforcement Learning, Model-Free Reinforcement Learning, Value Function, Policy Function, Reward Function, State Transition Function.

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References

2021

• (Patel et al., 2021) ⇒ Sahil Patel, Ewoud Vos, and Henk Wymeersch. (2021). “Robust Deep Reinforcement Learning for Quadcopter Control.” In: arXiv preprint arXiv:2111.03915. [URL](https://ar5iv.org/abs/2111.03915)
  • NOTES: It introduces the use of Robust Markov Decision Processes (RMDP) and the Action Robust Deep Deterministic Policy Gradient (AR-DDPG) algorithm for robust drone control, demonstrating advanced RL techniques for handling uncertainties in quadcopter flight tasks.

2022

• (Timmerman et al., 2022) ⇒ Mike Timmerman, Aryan Patel, and Tim Reinhart. (2022). “Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control.” In: arXiv preprint arXiv:2403.07216. [URL](https://ar5iv.org/abs/2403.07216)
  • NOTES: It discusses applying reinforcement learning to dynamically adjust the gains of a quadcopter controller, showcasing how RL can optimize robot control systems for improved performance and adaptability.

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