Reinforcement Learning Task: Difference between revisions
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A [[Reinforcement Learning Task]] is an [[ | 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]]). | ||
* <B> | * <B>Context:</B> | ||
** 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 | ** It can range from being a [[Discreate-Space Reinforcement Learning Task]] to being a [[Continuous-Space Reinforcement Learning Task]]. | ||
* <B | ** … | ||
** | * <B>Example(s):</B> | ||
** a [[ | ** an [[RL-based Autonomous Helicopter Flight Task]], as presented in the paper "Robust Deep Reinforcement Learning for Quadcopter Control". | ||
** <B>Counter-Example(s):</B> | ** 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]], ... | ||
* <B | ** 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]]. | |||
** … | |||
* <B>Counter-Example(s):</B> | |||
** 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. | |||
* <B>See:</B> [[Model-Based Reinforcement Learning]], [[Model-Free Reinforcement Learning]], [[Value Function]], [[Policy Function]], [[Reward Function]], [[State Transition Function]]. | |||
---- | ---- | ||
---- | ---- | ||
=== | == 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. | |||
---- | ---- | ||
__NOTOC__ | __NOTOC__ | ||
[[Category:Quality Silver]] | |||
[[Category:Concept]] | [[Category:Concept]] | ||
Latest revision as of 06:52, 23 September 2024
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.
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.