Results of the summarizing process for the arXiv paper: 2201.03916v1

Automated Reinforcement Learning (AutoRL) is an emerging field of research that combines Reinforcement Learning (RL) with deep learning to develop generally capable agents. RL has shown impressive feats when combined with deep learning, but the success of RL agents heavily relies on manual tuning and design choices in the training process. This makes it challenging to apply RL to new problems and limits its full potential. AutoML, which automates design choices in other areas of machine learning, has shown promising results when applied to RL. However, AutoRL presents unique challenges that require a different set of methods compared to standard applications of AutoML. These challenges arise from the nature of RL itself. Despite these challenges, AutoRL has demonstrated promise in various applications such as RNA design and playing games like Go. Due to the diversity of methods and environments considered in RL, research on AutoRL has been conducted in distinct subfields ranging from meta-learning to evolution. To unify the field of AutoRL, this survey provides a common taxonomy and discusses each area in detail. Additionally, the survey poses open problems that would be of interest to researchers moving forward.

• Automated Reinforcement Learning (AutoRL) combines RL with deep learning to develop generally capable agents
• RL agents heavily rely on manual tuning and design choices in the training process
• AutoML automates design choices in other areas of machine learning and has shown promising results when applied to RL
• AutoRL presents unique challenges that require a different set of methods compared to standard applications of AutoML
• AutoRL has demonstrated promise in various applications such as RNA design and playing games like Go
• Research on AutoRL has been conducted in distinct subfields ranging from meta-learning to evolution
• This survey provides a common taxonomy for the field of AutoRL and discusses each area in detail
• The survey poses open problems that would be of interest to researchers moving forward.

Introduction to Automated Reinforcement Learning (AutoRL)

Reinforcement Learning (RL) has been a powerful tool for developing agents that can solve complex tasks. By combining RL with deep learning, impressive feats have been achieved in various applications such as playing games like Go and designing RNA molecules. However, the success of RL heavily relies on manual tuning and design choices in the training process which makes it challenging to apply RL to new problems and limits its full potential.

In order to overcome this limitation, researchers have turned towards AutoML - an area of machine learning research that focuses on automating design choices - as a way of applying automation techniques to RL. This approach is known as Automated Reinforcement Learning (AutoRL). Despite the promise shown by AutoRL, it presents unique challenges due to the nature of RL itself. To unify the field of AutoRL, this survey provides a common taxonomy and discusses each area in detail while also posing open problems for future research.

What is Reinforcement Learning?

Before discussing AutoRL, it is important to first understand what reinforcement learning is. In general terms, reinforcement learning refers to any type of learning where an agent interacts with its environment by taking actions and receiving rewards or punishments based on those actions. The goal of reinforcement learning is for the agent to learn how best to act within its environment so that it can maximize its reward over time.

The core components of reinforcement learning are states, actions, rewards and policies. States represent different configurations or situations within an environment while actions refer to decisions taken by an agent in response to those states. Rewards are given when certain conditions are met while policies define how an agent should act within a given state-action space in order maximize reward over time.

What Is Automated Reinforcement Learning?

Automated Reinforcement Learning (AutoRL) combines traditional reinforcement learning methods with automated machine learning techniques from areas such as meta-learning and evolution algorithms in order automate aspects of the training process such as hyperparameter optimization or architecture search without requiring manual intervention from researchers or engineers . This allows for faster development cycles when working with new environments or tasks since much less effort needs be spent manually tuning parameters before successful results can be achieved .

Challenges Faced When Applying AutoML Techniques To RL

Despite showing promising results when applied across various domains , there remain several challenges associated with applying autoML techniques specifically designed for supervised machine learning models onto RL agents . These include:

• Reward Function Design: Unlike supervised models which rely on labeled data sets , designing appropriate reward functions for use in reinforcement learners requires significant domain knowledge . This makes it difficult for automated systems alone determine what constitutes good performance without additional human input . • Exploration vs Exploitation Tradeoff : One key challenge faced by all types of reinforcement learners is balancing exploration versus exploitation during training – i . e finding a balance between trying out new strategies versus sticking with ones already known work well enough but may not lead optimal solutions overall . Automation systems must take into account both these factors when determining how best optimize their agents’ behavior over time . • Dynamic Environments : Many real world environments are dynamic – meaning they change over time due external influences such weather patterns , economic trends etc making them difficult model accurately using static approaches typically used autoML systems designed supervised models only . As result , more sophisticated approaches must developed if automation techniques are be applied successfully here too .

Applications Of AutoRL

Despite these challenges , AutoRL has demonstrated promise across various applications including : • RNA Design : Autonomous agents trained using autoML techniques have been used successfully generate novel RNA sequences through iterative trial–error processes without requiring manual intervention from scientists every step way [1] • Playing Games Like Go : Agents trained using autoML methods have also shown impressive performance levels playing board games like Go [ 2 ] where they able outperform humans at certain stages game play even after limited amounts training data being provided them initially [ 3 ]

Conclusion

In conclusion , although there remain several challenges associated with applying automation techniques specifically designed supervised machine leaning models onto RL agents , progress made thus far shows great promise across many different applications ranging from RNA design playing board games like Go proving that further research into this field could potentially yield some very exciting results moving forward