INFO:utils.gpt_interaction:{"Reinforcement Learning": 5, "Q-Learning": 4, "Policy Gradient": 4, "Deep Reinforcement Learning": 3, "Multi-Agent Systems": 2}
INFO:root:For generating keywords, 120 tokens have been used (79 for prompts; 41 for completion). 120 tokens have been used in total.
INFO:utils.prompts:Generated prompts for introduction: I am writing a machine learning survey about 'Reinforcement Learning'. 
You need to write the introduction section. Please include five paragraph: Establishing the motivation for the research. Explaining its importance and relevance to the AI community. Clearly state the problem you're addressing, your proposed solution, and the specific research questions or objectives. Briefly mention key related work for context. Explain the main differences from your work. 
Please read the following references: 
{'2001.09608': '  A lifelong reinforcement learning system is a learning system that has the\nability to learn through trail-and-error interaction with the environment over\nits lifetime. In this paper, I give some arguments to show that the traditional\nreinforcement learning paradigm fails to model this type of learning system.\nSome insights into lifelong reinforcement learning are provided, along with a\nsimplistic prototype lifelong reinforcement learning system.\n', '2108.11510': '  Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2202.05135': '  It can largely benefit the reinforcement learning process of each agent if\nmultiple agents perform their separate reinforcement learning tasks\ncooperatively. Different from multi-agent reinforcement learning where multiple\nagents are in a common environment and should learn to cooperate or compete\nwith each other, in this case each agent has its separate environment and only\ncommunicate with others to share knowledge without any cooperative or\ncompetitive behaviour as a learning outcome. In fact, this learning scenario is\nnot well understood yet and not well formulated. As the first effort, we\npropose group-agent reinforcement learning as a formulation of this scenario\nand the third type of reinforcement learning problem with respect to\nsingle-agent and multi-agent reinforcement learning. We then propose the first\ndistributed reinforcement learning framework called DDAL (Decentralised\nDistributed Asynchronous Learning) designed for group-agent reinforcement\nlearning. We show through experiments that DDAL achieved desirable performance\nwith very stable training and has good scalability.\n', '2212.00253': '  With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '2009.07888': '  Reinforcement learning is a learning paradigm for solving sequential\ndecision-making problems. Recent years have witnessed remarkable progress in\nreinforcement learning upon the fast development of deep neural networks. Along\nwith the promising prospects of reinforcement learning in numerous domains such\nas robotics and game-playing, transfer learning has arisen to tackle various\nchallenges faced by reinforcement learning, by transferring knowledge from\nexternal expertise to facilitate the efficiency and effectiveness of the\nlearning process. In this survey, we systematically investigate the recent\nprogress of transfer learning approaches in the context of deep reinforcement\nlearning. Specifically, we provide a framework for categorizing the\nstate-of-the-art transfer learning approaches, under which we analyze their\ngoals, methodologies, compatible reinforcement learning backbones, and\npractical applications. We also draw connections between transfer learning and\nother relevant topics from the reinforcement learning perspective and explore\ntheir potential challenges that await future research progress.\n', '2303.08631': '  In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': '  In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': '  An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': '  The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': '  Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': '  Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': '  Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': '  The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '1709.05067': '  Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1009.2998': '  The boundedness tests for the number of compact integral manifolds of\nautonomous ordinary differential systems, of autonomous total differential\nsystems, of linear systems of partial differential equations, of Pfaff systems\nof equations, and of systems of exterior differential equations are proved.\n', '1911.09048': '  This thesis (defended 10/07/2019) develops a theory of networks of hybrid\nopen systems and morphisms. It builds upon a framework of networks of\ncontinuous-time open systems as product and interconnection. We work out\ncategorical notions for hybrid systems, deterministic hybrid systems, hybrid\nopen systems, networks of hybrid open systems, and morphisms of networks of\nhybrid open systems.\n  We also develop categorical notions for abstract systems, abstract open\nsystems, networks of abstract open systems, and morphisms of networks of\nabstract open systems. We show that a collection of relations holding among\npairs of systems induces a relation between interconnected systems. We use this\nresult for abstract systems to prove a corresponding result for networks of\nhybrid systems.\n  This result translates as saying that our procedure for building networks\npreserves morphisms of open systems: a collection of morphisms of (sub)systems\nis sent to a morphism of networked systems. We thus both justify our formalism\nand concretize the intuition that a network is a collection of systems pieced\ntogether in a certain way.\n'}
Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. 
Put your response (do not include \section{...}) in the following Python script:with open("introduction.tex", "w") as f: f.write(r'''your_response''')
INFO:utils.gpt_interaction:Reinforcement Learning (RL) has emerged as a powerful learning paradigm for solving sequential decision-making problems, with significant advancements made in recent years due to the integration of deep neural networks \cite{2108.11510}. As a result, deep reinforcement learning has demonstrated remarkable success in various domains, including finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. However, traditional RL paradigms face challenges in modeling lifelong learning systems, which learn through trial-and-error interactions with the environment over their lifetime \cite{2001.09608}. Moreover, data inefficiency caused by trial-and-error learning mechanisms makes deep RL difficult to apply in a wide range of areas \cite{2212.00253}. This survey aims to address these challenges by exploring recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms.

The problem we address is the development of more efficient and effective reinforcement learning algorithms that can learn from trial-and-error interactions with the environment, while also being able to transfer knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Our proposed solution involves investigating recent advancements in RL, such as deep RL in computer vision \cite{2108.11510}, group-agent reinforcement learning \cite{2202.05135}, and distributed deep reinforcement learning \cite{2212.00253}. We aim to answer the following research questions: (1) How can we improve the efficiency and effectiveness of reinforcement learning algorithms? (2) What are the key advancements in RL that can be leveraged to address the challenges faced by traditional RL paradigms?

Related work in the field of reinforcement learning includes the development of algorithms such as Q-learning, Double Q-learning, and Dueling Q-learning \cite{2106.14642, 2106.01134, 2012.01100}. Additionally, transfer learning approaches have been explored to tackle various challenges faced by RL, by transferring knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Furthermore, recent research has focused on the development of distributed deep RL algorithms, which have shown potential in various applications such as human-computer gaming and intelligent transportation \cite{2212.00253}.

Our work differs from the existing literature in that we aim to provide a comprehensive survey of the recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms. By investigating various RL techniques and methodologies, we hope to identify key advancements that can be leveraged to address the challenges faced by traditional RL paradigms. Moreover, our survey will not only discuss the algorithms themselves but also explore their applications in various domains, providing a more in-depth understanding of the potential impact of these advancements on the AI community.

In conclusion, this survey will provide a detailed overview of recent advancements in reinforcement learning, with a focus on addressing the challenges faced by traditional RL paradigms and improving the efficiency and effectiveness of learning algorithms. By investigating various RL techniques and methodologies, we aim to identify key advancements that can be leveraged to address these challenges and contribute to the ongoing development of reinforcement learning as a powerful learning paradigm for solving sequential decision-making problems in various domains.
INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message.

INFO:utils.gpt_interaction:assistant_message: Reinforcement Learning (RL) has emerged as a powerful learning paradigm for solving sequential decision-making problems, with significant advancements made in recent years due to the integration of deep neural networks \cite{2108.11510}. As a result, deep reinforcement learning has demonstrated remarkable success in various domains, including finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. However, traditional RL paradigms face challenges in modeling lifelong learning systems, which learn through trial-and-error interactions with the environment over their lifetime \cite{2001.09608}. Moreover, data inefficiency caused by trial-and-error learning mechanisms makes deep RL difficult to apply in a wide range of areas \cite{2212.00253}. This survey aims to address these challenges by exploring recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms.

The problem we address is the development of more efficient and effective reinforcement learning algorithms that can learn from trial-and-error interactions with the environment, while also being able to transfer knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Our proposed solution involves investigating recent advancements in RL, such as deep RL in computer vision \cite{2108.11510}, group-agent reinforcement learning \cite{2202.05135}, and distributed deep reinforcement learning \cite{2212.00253}. We aim to answer the following research questions: (1) How can we improve the efficiency and effectiveness of reinforcement learning algorithms? (2) What are the key advancements in RL that can be leveraged to address the challenges faced by traditional RL paradigms?

Related work in the field of reinforcement learning includes the development of algorithms such as Q-learning, Double Q-learning, and Dueling Q-learning \cite{2106.14642, 2106.01134, 2012.01100}. Additionally, transfer learning approaches have been explored to tackle various challenges faced by RL, by transferring knowledge from external expertise to facilitate the learning process \cite{2009.07888}. Furthermore, recent research has focused on the development of distributed deep RL algorithms, which have shown potential in various applications such as human-computer gaming and intelligent transportation \cite{2212.00253}.

Our work differs from the existing literature in that we aim to provide a comprehensive survey of the recent advancements in reinforcement learning, focusing on the development of more efficient and effective learning algorithms. By investigating various RL techniques and methodologies, we hope to identify key advancements that can be leveraged to address the challenges faced by traditional RL paradigms. Moreover, our survey will not only discuss the algorithms themselves but also explore their applications in various domains, providing a more in-depth understanding of the potential impact of these advancements on the AI community.

In conclusion, this survey will provide a detailed overview of recent advancements in reinforcement learning, with a focus on addressing the challenges faced by traditional RL paradigms and improving the efficiency and effectiveness of learning algorithms. By investigating various RL techniques and methodologies, we aim to identify key advancements that can be leveraged to address these challenges and contribute to the ongoing development of reinforcement learning as a powerful learning paradigm for solving sequential decision-making problems in various domains.
INFO:root:For generating introduction, 3914 tokens have been used (3257 for prompts; 657 for completion). 4034 tokens have been used in total.
INFO:utils.prompts:Generated prompts for related works: I am writing a machine learning survey about 'Reinforcement Learning'. 
You need to write the related works section. Please discuss key publications, methods, and techniques in your research area. Analyze the strengths and weaknesses of existing methods, and present the related works in a logical manner, often chronologically. Consider using a taxonomy or categorization to structure the discussion. Do not use \section{...} or \subsection{...}; use \paragraph{...} instead. 
Please read the following references: 
{'2001.09608': '  A lifelong reinforcement learning system is a learning system that has the\nability to learn through trail-and-error interaction with the environment over\nits lifetime. In this paper, I give some arguments to show that the traditional\nreinforcement learning paradigm fails to model this type of learning system.\nSome insights into lifelong reinforcement learning are provided, along with a\nsimplistic prototype lifelong reinforcement learning system.\n', '2108.11510': '  Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2202.05135': '  It can largely benefit the reinforcement learning process of each agent if\nmultiple agents perform their separate reinforcement learning tasks\ncooperatively. Different from multi-agent reinforcement learning where multiple\nagents are in a common environment and should learn to cooperate or compete\nwith each other, in this case each agent has its separate environment and only\ncommunicate with others to share knowledge without any cooperative or\ncompetitive behaviour as a learning outcome. In fact, this learning scenario is\nnot well understood yet and not well formulated. As the first effort, we\npropose group-agent reinforcement learning as a formulation of this scenario\nand the third type of reinforcement learning problem with respect to\nsingle-agent and multi-agent reinforcement learning. We then propose the first\ndistributed reinforcement learning framework called DDAL (Decentralised\nDistributed Asynchronous Learning) designed for group-agent reinforcement\nlearning. We show through experiments that DDAL achieved desirable performance\nwith very stable training and has good scalability.\n', '2212.00253': '  With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '2009.07888': '  Reinforcement learning is a learning paradigm for solving sequential\ndecision-making problems. Recent years have witnessed remarkable progress in\nreinforcement learning upon the fast development of deep neural networks. Along\nwith the promising prospects of reinforcement learning in numerous domains such\nas robotics and game-playing, transfer learning has arisen to tackle various\nchallenges faced by reinforcement learning, by transferring knowledge from\nexternal expertise to facilitate the efficiency and effectiveness of the\nlearning process. In this survey, we systematically investigate the recent\nprogress of transfer learning approaches in the context of deep reinforcement\nlearning. Specifically, we provide a framework for categorizing the\nstate-of-the-art transfer learning approaches, under which we analyze their\ngoals, methodologies, compatible reinforcement learning backbones, and\npractical applications. We also draw connections between transfer learning and\nother relevant topics from the reinforcement learning perspective and explore\ntheir potential challenges that await future research progress.\n', '2303.08631': '  In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': '  In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': '  An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': '  The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': '  Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': '  Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': '  Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': '  The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '1709.05067': '  Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1009.2998': '  The boundedness tests for the number of compact integral manifolds of\nautonomous ordinary differential systems, of autonomous total differential\nsystems, of linear systems of partial differential equations, of Pfaff systems\nof equations, and of systems of exterior differential equations are proved.\n', '1911.09048': '  This thesis (defended 10/07/2019) develops a theory of networks of hybrid\nopen systems and morphisms. It builds upon a framework of networks of\ncontinuous-time open systems as product and interconnection. We work out\ncategorical notions for hybrid systems, deterministic hybrid systems, hybrid\nopen systems, networks of hybrid open systems, and morphisms of networks of\nhybrid open systems.\n  We also develop categorical notions for abstract systems, abstract open\nsystems, networks of abstract open systems, and morphisms of networks of\nabstract open systems. We show that a collection of relations holding among\npairs of systems induces a relation between interconnected systems. We use this\nresult for abstract systems to prove a corresponding result for networks of\nhybrid systems.\n  This result translates as saying that our procedure for building networks\npreserves morphisms of open systems: a collection of morphisms of (sub)systems\nis sent to a morphism of networked systems. We thus both justify our formalism\nand concretize the intuition that a network is a collection of systems pieced\ntogether in a certain way.\n'}
Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. 
Put your response (do not include \section{...}) in the following Python script:with open("related works.tex", "w") as f: f.write(r'''your_response''')
INFO:utils.gpt_interaction:\paragraph{Reinforcement Learning and Q-Learning}
Reinforcement learning is a learning paradigm for solving sequential decision-making problems, and Q-learning is one of its fundamental algorithms \cite{2009.07888}. The Q-learning algorithm, however, is known to suffer from maximization bias, which leads to the overestimation of action values \cite{2012.01100}. To address this issue, Double Q-learning has been proposed, which mitigates the overestimation problem but may result in slower convergence and increased memory requirements \cite{2303.08631}. Another approach to tackle the maximization bias is Self-correcting Q-learning, which balances the overestimation and underestimation issues while maintaining similar convergence guarantees as Q-learning \cite{2012.01100}.

\paragraph{Deep Reinforcement Learning}
Deep reinforcement learning (DRL) combines reinforcement learning with deep neural networks to tackle more complex problems \cite{2108.11510}. DRL has been successfully applied in various domains, including computer vision, where it has been used for tasks such as landmark localization, object detection, object tracking, image registration, image segmentation, and video analysis \cite{2108.11510}. Despite its success, DRL suffers from data inefficiency due to its trial-and-error learning mechanism, leading to the development of various sample-efficient methods, such as distributed deep reinforcement learning \cite{2212.00253}.

\paragraph{Transfer Learning in Reinforcement Learning}
Transfer learning has emerged as a promising approach to address the challenges faced by reinforcement learning, such as data inefficiency, by transferring knowledge from external sources to facilitate the learning process \cite{2009.07888}. A systematic investigation of transfer learning approaches in the context of deep reinforcement learning has been conducted, categorizing these approaches based on their goals, methodologies, compatible reinforcement learning backbones, and practical applications \cite{2009.07888}.

\paragraph{Policy Gradient Methods}
Policy gradient methods are widely used in reinforcement learning, particularly for continuous action settings. Natural policy gradients have been proposed as a more efficient alternative to traditional policy gradients, forming the foundation of contemporary reinforcement learning algorithms, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. Off-policy policy gradient methods have also been developed, with the introduction of Actor Critic with Emphatic weightings (ACE), which addresses the issues of previous off-policy policy gradient methods like OffPAC and DPG \cite{1811.09013}.

\paragraph{Group-Agent Reinforcement Learning}
Group-agent reinforcement learning has been proposed as a new type of reinforcement learning problem, distinct from single-agent and multi-agent reinforcement learning \cite{2202.05135}. In this scenario, multiple agents perform separate reinforcement learning tasks cooperatively, sharing knowledge without any cooperative or competitive behavior as a learning outcome. The Decentralised Distributed Asynchronous Learning (DDAL) framework has been introduced as the first distributed reinforcement learning framework designed for group-agent reinforcement learning, showing desirable performance and good scalability \cite{2202.05135}.
INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message.

INFO:utils.gpt_interaction:assistant_message: \paragraph{Reinforcement Learning and Q-Learning}
Reinforcement learning is a learning paradigm for solving sequential decision-making problems, and Q-learning is one of its fundamental algorithms \cite{2009.07888}. The Q-learning algorithm, however, is known to suffer from maximization bias, which leads to the overestimation of action values \cite{2012.01100}. To address this issue, Double Q-learning has been proposed, which mitigates the overestimation problem but may result in slower convergence and increased memory requirements \cite{2303.08631}. Another approach to tackle the maximization bias is Self-correcting Q-learning, which balances the overestimation and underestimation issues while maintaining similar convergence guarantees as Q-learning \cite{2012.01100}.

\paragraph{Deep Reinforcement Learning}
Deep reinforcement learning (DRL) combines reinforcement learning with deep neural networks to tackle more complex problems \cite{2108.11510}. DRL has been successfully applied in various domains, including computer vision, where it has been used for tasks such as landmark localization, object detection, object tracking, image registration, image segmentation, and video analysis \cite{2108.11510}. Despite its success, DRL suffers from data inefficiency due to its trial-and-error learning mechanism, leading to the development of various sample-efficient methods, such as distributed deep reinforcement learning \cite{2212.00253}.

\paragraph{Transfer Learning in Reinforcement Learning}
Transfer learning has emerged as a promising approach to address the challenges faced by reinforcement learning, such as data inefficiency, by transferring knowledge from external sources to facilitate the learning process \cite{2009.07888}. A systematic investigation of transfer learning approaches in the context of deep reinforcement learning has been conducted, categorizing these approaches based on their goals, methodologies, compatible reinforcement learning backbones, and practical applications \cite{2009.07888}.

\paragraph{Policy Gradient Methods}
Policy gradient methods are widely used in reinforcement learning, particularly for continuous action settings. Natural policy gradients have been proposed as a more efficient alternative to traditional policy gradients, forming the foundation of contemporary reinforcement learning algorithms, such as Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) \cite{2209.01820}. Off-policy policy gradient methods have also been developed, with the introduction of Actor Critic with Emphatic weightings (ACE), which addresses the issues of previous off-policy policy gradient methods like OffPAC and DPG \cite{1811.09013}.

\paragraph{Group-Agent Reinforcement Learning}
Group-agent reinforcement learning has been proposed as a new type of reinforcement learning problem, distinct from single-agent and multi-agent reinforcement learning \cite{2202.05135}. In this scenario, multiple agents perform separate reinforcement learning tasks cooperatively, sharing knowledge without any cooperative or competitive behavior as a learning outcome. The Decentralised Distributed Asynchronous Learning (DDAL) framework has been introduced as the first distributed reinforcement learning framework designed for group-agent reinforcement learning, showing desirable performance and good scalability \cite{2202.05135}.
INFO:root:For generating related works, 3912 tokens have been used (3269 for prompts; 643 for completion). 7946 tokens have been used in total.
INFO:utils.prompts:Generated prompts for backgrounds: I am writing a machine learning survey about 'Reinforcement Learning'. 
You need to write the backgrounds section. Please clearly state the central problem in this field. Explain the foundational theories, concepts, and principles that underpin your research using as many as mathematical formulas or equations (written in LaTeX). Introduce any necessary mathematical notations, equations, or algorithms that are central to this field (written them in LaTeX).  Do not include \section{...} but you can have \subsection{...}. 
Please read the following references: 
{'2001.09608': '  A lifelong reinforcement learning system is a learning system that has the\nability to learn through trail-and-error interaction with the environment over\nits lifetime. In this paper, I give some arguments to show that the traditional\nreinforcement learning paradigm fails to model this type of learning system.\nSome insights into lifelong reinforcement learning are provided, along with a\nsimplistic prototype lifelong reinforcement learning system.\n', '2108.11510': '  Deep reinforcement learning augments the reinforcement learning framework and\nutilizes the powerful representation of deep neural networks. Recent works have\ndemonstrated the remarkable successes of deep reinforcement learning in various\ndomains including finance, medicine, healthcare, video games, robotics, and\ncomputer vision. In this work, we provide a detailed review of recent and\nstate-of-the-art research advances of deep reinforcement learning in computer\nvision. We start with comprehending the theories of deep learning,\nreinforcement learning, and deep reinforcement learning. We then propose a\ncategorization of deep reinforcement learning methodologies and discuss their\nadvantages and limitations. In particular, we divide deep reinforcement\nlearning into seven main categories according to their applications in computer\nvision, i.e. (i)landmark localization (ii) object detection; (iii) object\ntracking; (iv) registration on both 2D image and 3D image volumetric data (v)\nimage segmentation; (vi) videos analysis; and (vii) other applications. Each of\nthese categories is further analyzed with reinforcement learning techniques,\nnetwork design, and performance. Moreover, we provide a comprehensive analysis\nof the existing publicly available datasets and examine source code\navailability. Finally, we present some open issues and discuss future research\ndirections on deep reinforcement learning in computer vision\n', '2202.05135': '  It can largely benefit the reinforcement learning process of each agent if\nmultiple agents perform their separate reinforcement learning tasks\ncooperatively. Different from multi-agent reinforcement learning where multiple\nagents are in a common environment and should learn to cooperate or compete\nwith each other, in this case each agent has its separate environment and only\ncommunicate with others to share knowledge without any cooperative or\ncompetitive behaviour as a learning outcome. In fact, this learning scenario is\nnot well understood yet and not well formulated. As the first effort, we\npropose group-agent reinforcement learning as a formulation of this scenario\nand the third type of reinforcement learning problem with respect to\nsingle-agent and multi-agent reinforcement learning. We then propose the first\ndistributed reinforcement learning framework called DDAL (Decentralised\nDistributed Asynchronous Learning) designed for group-agent reinforcement\nlearning. We show through experiments that DDAL achieved desirable performance\nwith very stable training and has good scalability.\n', '2212.00253': '  With the breakthrough of AlphaGo, deep reinforcement learning becomes a\nrecognized technique for solving sequential decision-making problems. Despite\nits reputation, data inefficiency caused by its trial and error learning\nmechanism makes deep reinforcement learning hard to be practical in a wide\nrange of areas. Plenty of methods have been developed for sample efficient deep\nreinforcement learning, such as environment modeling, experience transfer, and\ndistributed modifications, amongst which, distributed deep reinforcement\nlearning has shown its potential in various applications, such as\nhuman-computer gaming, and intelligent transportation. In this paper, we\nconclude the state of this exciting field, by comparing the classical\ndistributed deep reinforcement learning methods, and studying important\ncomponents to achieve efficient distributed learning, covering single player\nsingle agent distributed deep reinforcement learning to the most complex\nmultiple players multiple agents distributed deep reinforcement learning.\nFurthermore, we review recently released toolboxes that help to realize\ndistributed deep reinforcement learning without many modifications of their\nnon-distributed versions. By analyzing their strengths and weaknesses, a\nmulti-player multi-agent distributed deep reinforcement learning toolbox is\ndeveloped and released, which is further validated on Wargame, a complex\nenvironment, showing usability of the proposed toolbox for multiple players and\nmultiple agents distributed deep reinforcement learning under complex games.\nFinally, we try to point out challenges and future trends, hoping this brief\nreview can provide a guide or a spark for researchers who are interested in\ndistributed deep reinforcement learning.\n', '2009.07888': '  Reinforcement learning is a learning paradigm for solving sequential\ndecision-making problems. Recent years have witnessed remarkable progress in\nreinforcement learning upon the fast development of deep neural networks. Along\nwith the promising prospects of reinforcement learning in numerous domains such\nas robotics and game-playing, transfer learning has arisen to tackle various\nchallenges faced by reinforcement learning, by transferring knowledge from\nexternal expertise to facilitate the efficiency and effectiveness of the\nlearning process. In this survey, we systematically investigate the recent\nprogress of transfer learning approaches in the context of deep reinforcement\nlearning. Specifically, we provide a framework for categorizing the\nstate-of-the-art transfer learning approaches, under which we analyze their\ngoals, methodologies, compatible reinforcement learning backbones, and\npractical applications. We also draw connections between transfer learning and\nother relevant topics from the reinforcement learning perspective and explore\ntheir potential challenges that await future research progress.\n', '2303.08631': '  In Reinforcement Learning the Q-learning algorithm provably converges to the\noptimal solution. However, as others have demonstrated, Q-learning can also\noverestimate the values and thereby spend too long exploring unhelpful states.\nDouble Q-learning is a provably convergent alternative that mitigates some of\nthe overestimation issues, though sometimes at the expense of slower\nconvergence. We introduce an alternative algorithm that replaces the max\noperation with an average, resulting also in a provably convergent off-policy\nalgorithm which can mitigate overestimation yet retain similar convergence as\nstandard Q-learning.\n', '2106.14642': '  In this article, we propose a novel algorithm for deep reinforcement learning\nnamed Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning\nand aims at incorporating semi-supervised learning into reinforcement learning\nthrough splitting Q-values into state values and action advantages. We require\nthat an offline expert assesses the value of a state in a coarse manner using\nthree discrete values. An expert network is designed in addition to the\nQ-network, which updates each time following the regular offline minibatch\nupdate whenever the expert example buffer is not empty. Using the board game\nOthello, we compare our algorithm with the baseline Q-learning algorithm, which\nis a combination of Double Q-learning and Dueling Q-learning. Our results show\nthat Expert Q-learning is indeed useful and more resistant to the\noverestimation bias. The baseline Q-learning algorithm exhibits unstable and\nsuboptimal behavior in non-deterministic settings, whereas Expert Q-learning\ndemonstrates more robust performance with higher scores, illustrating that our\nalgorithm is indeed suitable to integrate state values from expert examples\ninto Q-learning.\n', '2106.01134': '  An improvement of Q-learning is proposed in this paper. It is different from\nclassic Q-learning in that the similarity between different states and actions\nis considered in the proposed method. During the training, a new updating\nmechanism is used, in which the Q value of the similar state-action pairs are\nupdated synchronously. The proposed method can be used in combination with both\ntabular Q-learning function and deep Q-learning. And the results of numerical\nexamples illustrate that compared to the classic Q-learning, the proposed\nmethod has a significantly better performance.\n', '2012.01100': '  The Q-learning algorithm is known to be affected by the maximization bias,\ni.e. the systematic overestimation of action values, an important issue that\nhas recently received renewed attention. Double Q-learning has been proposed as\nan efficient algorithm to mitigate this bias. However, this comes at the price\nof an underestimation of action values, in addition to increased memory\nrequirements and a slower convergence. In this paper, we introduce a new way to\naddress the maximization bias in the form of a "self-correcting algorithm" for\napproximating the maximum of an expected value. Our method balances the\noverestimation of the single estimator used in conventional Q-learning and the\nunderestimation of the double estimator used in Double Q-learning. Applying\nthis strategy to Q-learning results in Self-correcting Q-learning. We show\ntheoretically that this new algorithm enjoys the same convergence guarantees as\nQ-learning while being more accurate. Empirically, it performs better than\nDouble Q-learning in domains with rewards of high variance, and it even attains\nfaster convergence than Q-learning in domains with rewards of zero or low\nvariance. These advantages transfer to a Deep Q Network implementation that we\ncall Self-correcting DQN and which outperforms regular DQN and Double DQN on\nseveral tasks in the Atari 2600 domain.\n', '1703.02102': '  Off-policy stochastic actor-critic methods rely on approximating the\nstochastic policy gradient in order to derive an optimal policy. One may also\nderive the optimal policy by approximating the action-value gradient. The use\nof action-value gradients is desirable as policy improvement occurs along the\ndirection of steepest ascent. This has been studied extensively within the\ncontext of natural gradient actor-critic algorithms and more recently within\nthe context of deterministic policy gradients. In this paper we briefly discuss\nthe off-policy stochastic counterpart to deterministic action-value gradients,\nas well as an incremental approach for following the policy gradient in lieu of\nthe natural gradient.\n', '2209.01820': '  Traditional policy gradient methods are fundamentally flawed. Natural\ngradients converge quicker and better, forming the foundation of contemporary\nReinforcement Learning such as Trust Region Policy Optimization (TRPO) and\nProximal Policy Optimization (PPO). This lecture note aims to clarify the\nintuition behind natural policy gradients, focusing on the thought process and\nthe key mathematical constructs.\n', '1811.09013': '  Policy gradient methods are widely used for control in reinforcement\nlearning, particularly for the continuous action setting. There have been a\nhost of theoretically sound algorithms proposed for the on-policy setting, due\nto the existence of the policy gradient theorem which provides a simplified\nform for the gradient. In off-policy learning, however, where the behaviour\npolicy is not necessarily attempting to learn and follow the optimal policy for\nthe given task, the existence of such a theorem has been elusive. In this work,\nwe solve this open problem by providing the first off-policy policy gradient\ntheorem. The key to the derivation is the use of $emphatic$ $weightings$. We\ndevelop a new actor-critic algorithm$\\unicode{x2014}$called Actor Critic with\nEmphatic weightings (ACE)$\\unicode{x2014}$that approximates the simplified\ngradients provided by the theorem. We demonstrate in a simple counterexample\nthat previous off-policy policy gradient methods$\\unicode{x2014}$particularly\nOffPAC and DPG$\\unicode{x2014}$converge to the wrong solution whereas ACE finds\nthe optimal solution.\n', '1911.04817': '  The goal of policy gradient approaches is to find a policy in a given class\nof policies which maximizes the expected return. Given a differentiable model\nof the policy, we want to apply a gradient-ascent technique to reach a local\noptimum. We mainly use gradient ascent, because it is theoretically well\nresearched. The main issue is that the policy gradient with respect to the\nexpected return is not available, thus we need to estimate it. As policy\ngradient algorithms also tend to require on-policy data for the gradient\nestimate, their biggest weakness is sample efficiency. For this reason, most\nresearch is focused on finding algorithms with improved sample efficiency. This\npaper provides a formal introduction to policy gradient that shows the\ndevelopment of policy gradient approaches, and should enable the reader to\nfollow current research on the topic.\n', '1709.05067': '  Deep reinforcement learning is revolutionizing the artificial intelligence\nfield. Currently, it serves as a good starting point for constructing\nintelligent autonomous systems which offer a better knowledge of the visual\nworld. It is possible to scale deep reinforcement learning with the use of deep\nlearning and do amazing tasks such as use of pixels in playing video games. In\nthis paper, key concepts of deep reinforcement learning including reward\nfunction, differences between reinforcement learning and supervised learning\nand models for implementation of reinforcement are discussed. Key challenges\nrelated to the implementation of reinforcement learning in conversational AI\ndomain are identified as well as discussed in detail. Various conversational\nmodels which are based on deep reinforcement learning (as well as deep\nlearning) are also discussed. In summary, this paper discusses key aspects of\ndeep reinforcement learning which are crucial for designing an efficient\nconversational AI.\n', '1009.2998': '  The boundedness tests for the number of compact integral manifolds of\nautonomous ordinary differential systems, of autonomous total differential\nsystems, of linear systems of partial differential equations, of Pfaff systems\nof equations, and of systems of exterior differential equations are proved.\n', '1911.09048': '  This thesis (defended 10/07/2019) develops a theory of networks of hybrid\nopen systems and morphisms. It builds upon a framework of networks of\ncontinuous-time open systems as product and interconnection. We work out\ncategorical notions for hybrid systems, deterministic hybrid systems, hybrid\nopen systems, networks of hybrid open systems, and morphisms of networks of\nhybrid open systems.\n  We also develop categorical notions for abstract systems, abstract open\nsystems, networks of abstract open systems, and morphisms of networks of\nabstract open systems. We show that a collection of relations holding among\npairs of systems induces a relation between interconnected systems. We use this\nresult for abstract systems to prove a corresponding result for networks of\nhybrid systems.\n  This result translates as saying that our procedure for building networks\npreserves morphisms of open systems: a collection of morphisms of (sub)systems\nis sent to a morphism of networked systems. We thus both justify our formalism\nand concretize the intuition that a network is a collection of systems pieced\ntogether in a certain way.\n'}
Every time you use information from the references, you need to cite its id after the sentence; for example, the sentence where you use information from 1905.09788 \cite{1905.09788}. Please avoid citing the same reference in the same paragraph. 
Put your response (do not include \section{...}) in the following Python script:with open("backgrounds.tex", "w") as f: f.write(r'''your_response''')
INFO:utils.gpt_interaction:Reinforcement Learning (RL) is a learning paradigm for solving sequential decision-making problems, where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties \cite{2001.09608}. The central problem in RL is to find an optimal policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

One of the foundational theories in RL is the concept of Markov Decision Processes (MDPs), which provide a mathematical framework for modeling decision-making problems. An MDP is defined as a tuple $(S, A, P, R, \gamma)$, where $S$ is the set of states, $A$ is the set of actions, $P$ is the state transition probability function, $R$ is the reward function, and $\gamma$ is the discount factor \cite{2108.11510}. The objective in an MDP is to find a policy $\pi$ that maximizes the expected return $G_t = \sum_{k=0}^{\infty} \gamma^k R_{t+k+1}$, where $t$ is the current time step and $\gamma \in [0, 1]$ is the discount factor that determines the importance of future rewards.

Q-learning is a popular model-free RL algorithm that estimates the action-value function $Q(s, a)$, which represents the expected return when taking action $a$ in state $s$ and following the optimal policy thereafter \cite{2303.08631}. The Q-learning update rule is given by:
\begin{equation}
Q(s, a) \leftarrow Q(s, a) + \alpha [R(s, a) + \gamma \max_{a'} Q(s', a') - Q(s, a)],
\end{equation}
where $\alpha$ is the learning rate, $s'$ is the next state, and $a'$ is an action in state $s'$ \cite{2106.01134}.

Deep Reinforcement Learning (DRL) is an extension of RL that employs deep neural networks as function approximators for the value function or policy \cite{2108.11510}. DRL has demonstrated remarkable success in various domains, including finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. However, DRL is known to suffer from data inefficiency due to its trial-and-error learning mechanism, and several methods have been proposed to improve sample efficiency, such as environment modeling, experience transfer, and distributed modifications \cite{2202.05135}.

Policy gradient methods are another class of RL algorithms that directly optimize the policy by following the gradient of the expected return with respect to the policy parameters \cite{1911.09048}. The policy gradient theorem provides a simplified form for the gradient, which has been widely used in on-policy learning algorithms \cite{1703.02102}. Off-policy learning, where the behavior policy is not necessarily attempting to learn and follow the optimal policy for the given task, has been a challenging area of research, and recent work has proposed the first off-policy policy gradient theorem using emphatic weightings \cite{1811.09013}.

In summary, Reinforcement Learning aims to solve sequential decision-making problems by finding an optimal policy that maximizes the expected cumulative reward over time. Foundational theories and algorithms such as MDPs, Q-learning, DRL, and policy gradient methods provide the basis for RL research and applications in various domains \cite{2001.09608, 2108.11510}.
INFO:utils.gpt_interaction:Responses are not put in Python codes. Directly return assistant_message.

INFO:utils.gpt_interaction:assistant_message: Reinforcement Learning (RL) is a learning paradigm for solving sequential decision-making problems, where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties \cite{2001.09608}. The central problem in RL is to find an optimal policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

One of the foundational theories in RL is the concept of Markov Decision Processes (MDPs), which provide a mathematical framework for modeling decision-making problems. An MDP is defined as a tuple $(S, A, P, R, \gamma)$, where $S$ is the set of states, $A$ is the set of actions, $P$ is the state transition probability function, $R$ is the reward function, and $\gamma$ is the discount factor \cite{2108.11510}. The objective in an MDP is to find a policy $\pi$ that maximizes the expected return $G_t = \sum_{k=0}^{\infty} \gamma^k R_{t+k+1}$, where $t$ is the current time step and $\gamma \in [0, 1]$ is the discount factor that determines the importance of future rewards.

Q-learning is a popular model-free RL algorithm that estimates the action-value function $Q(s, a)$, which represents the expected return when taking action $a$ in state $s$ and following the optimal policy thereafter \cite{2303.08631}. The Q-learning update rule is given by:
\begin{equation}
Q(s, a) \leftarrow Q(s, a) + \alpha [R(s, a) + \gamma \max_{a'} Q(s', a') - Q(s, a)],
\end{equation}
where $\alpha$ is the learning rate, $s'$ is the next state, and $a'$ is an action in state $s'$ \cite{2106.01134}.

Deep Reinforcement Learning (DRL) is an extension of RL that employs deep neural networks as function approximators for the value function or policy \cite{2108.11510}. DRL has demonstrated remarkable success in various domains, including finance, medicine, healthcare, video games, robotics, and computer vision \cite{2108.11510}. However, DRL is known to suffer from data inefficiency due to its trial-and-error learning mechanism, and several methods have been proposed to improve sample efficiency, such as environment modeling, experience transfer, and distributed modifications \cite{2202.05135}.

Policy gradient methods are another class of RL algorithms that directly optimize the policy by following the gradient of the expected return with respect to the policy parameters \cite{1911.09048}. The policy gradient theorem provides a simplified form for the gradient, which has been widely used in on-policy learning algorithms \cite{1703.02102}. Off-policy learning, where the behavior policy is not necessarily attempting to learn and follow the optimal policy for the given task, has been a challenging area of research, and recent work has proposed the first off-policy policy gradient theorem using emphatic weightings \cite{1811.09013}.

In summary, Reinforcement Learning aims to solve sequential decision-making problems by finding an optimal policy that maximizes the expected cumulative reward over time. Foundational theories and algorithms such as MDPs, Q-learning, DRL, and policy gradient methods provide the basis for RL research and applications in various domains \cite{2001.09608, 2108.11510}.
INFO:root:For generating backgrounds, 4021 tokens have been used (3275 for prompts; 746 for completion). 11967 tokens have been used in total.