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**Reinforcement learning** (**RL**) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

**Machine learning** (**ML**) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

In computer science, a **software agent ** is a computer program that acts for a user or other program in a relationship of agency, which derives from the Latin *agere* : an agreement to act on one's behalf. Such "action on behalf of" implies the authority to decide which, if any, action is appropriate. Agents are colloquially known as *bots*, from *robot*. They may be embodied, as when execution is paired with a robot body, or as software such as a chatbot executing on a phone or other computing device. Software agents may be autonomous or work together with other agents or people. Software agents interacting with people may possess human-like qualities such as natural language understanding and speech, personality or embody humanoid form.

**Action selection** is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior.

- Introduction
- Exploration
- Algorithms for control learning
- Criterion of optimality
- Brute force
- Value function
- Direct policy search
- Theory
- Research
- Comparison of reinforcement learning algorithms
- Deep reinforcement learning
- Inverse reinforcement learning
- Apprenticeship learning
- See also
- References
- Further reading
- External links

It differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).^{ [1] }

The environment is typically formulated as a Markov decision process (MDP), as many reinforcement learning algorithms for this context utilize dynamic programming techniques.^{ [2] } The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

A **Markov decision process** (**MDP**) is a discrete time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, *Dynamic Programming and Markov Processes*. They are used in many disciplines, including robotics, automatic control, economics and manufacturing. The name of MDPs comes from the Russian mathematician Andrey Markov.

**Dynamic programming** is both a mathematical optimization method and a computer programming method. The method was developed by Richard Bellman in the 1950s and has found applications in numerous fields, from aerospace engineering to economics. In both contexts it refers to simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner. While some decision problems cannot be taken apart this way, decisions that span several points in time do often break apart recursively. Likewise, in computer science, if a problem can be solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have optimal substructure.

Reinforcement learning, due to its generality, is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In the operations research and control literature, reinforcement learning is called *approximate dynamic programming,* or *neuro-dynamic programming.* The problems of interest in reinforcement learning have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment. In economics and game theory, reinforcement learning may be used to explain how equilibrium may arise under bounded rationality.

**Game theory** is the study of mathematical models of strategic interaction among rational decision-makers. It has applications in all fields of social science, as well as in logic, systems science, and computer science. Originally, it addressed zero-sum games, in which each participant's gains or losses are exactly balanced by those of the other participants. Today, game theory applies to a wide range of behavioral relations, and is now an umbrella term for the science of logical decision making in humans, animals, and computers.

**Control theory** in control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner without *delay or overshoot* and ensuring control stability.

**Operations research** (**OR**) is a discipline that deals with the application of advanced analytical methods to help make better decisions. Further, the term **operational analysis** is used in the British military as an intrinsic part of capability development, management and assurance. In particular, operational analysis forms part of the Combined Operational Effectiveness and Investment Appraisals, which support British defense capability acquisition decision-making.

Basic reinforcement is modeled as a Markov decision process:

- a set of environment and agent states, S;
- a set of actions, A, of the agent;
- is the probability of transition from state to state under action .
- is the immediate reward after transition from to with action .
- rules that describe what the agent observes

Rules are often stochastic. The observation typically involves the scalar, immediate reward associated with the last transition. In many works, the agent is assumed to observe the current environmental state (*full observability*). If not, the agent has *partial observability*. Sometimes the set of actions available to the agent is restricted (a zero balance cannot be reduced. For example, if the current value of the agent is 3 and the state transition reduces the value by 4, the transition will not be allowed).

**Stochastic** refers to a randomly determined process. The word first appeared in English to describe a mathematical object called a stochastic process, but now in mathematics the terms *stochastic process* and *random process* are considered interchangeable. The word, with its current definition meaning random, came from German, but it originally came from Greek στόχος* (stókhos)*, meaning 'aim, guess'.

A reinforcement learning agent interacts with its environment in discrete time steps. At each time t, the agent receives an observation , which typically includes the reward . It then chooses an action from the set of available actions, which is subsequently sent to the environment. The environment moves to a new state and the reward associated with the *transition* is determined. The goal of a reinforcement learning agent is to collect as much reward as possible. The agent can (possibly randomly) choose any action as a function of the history.

When the agent's performance is compared to that of an agent that acts optimally, the difference in performance gives rise to the notion of * regret *. In order to act near optimally, the agent must reason about the long term consequences of its actions (i.e., maximize future income), although the immediate reward associated with this might be negative.

Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off. It has been applied successfully to various problems, including robot control, elevator scheduling, telecommunications, backgammon, checkers ^{ [3] } and Go (AlphaGo).

Two elements make reinforcement learning powerful: the use of samples to optimize performance and the use of function approximation to deal with large environments. Thanks to these two key components, reinforcement learning can be used in large environments in the following situations:

- A model of the environment is known, but an analytic solution is not available;
- Only a simulation model of the environment is given (the subject of simulation-based optimization);
^{ [4] } - The only way to collect information about the environment is to interact with it.

The first two of these problems could be considered planning problems (since some form of model is available), while the last one could be considered to be a genuine learning problem. However, reinforcement learning converts both planning problems to machine learning problems.

The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space MDPs in Burnetas and Katehakis (1997).^{ [5] }

Reinforcement learning requires clever exploration mechanisms. Randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. The case of (small) finite Markov decision processes is relatively well understood. However, due to the lack of algorithms that scale well with the number of states (or scale to problems with infinite state spaces), simple exploration methods are the most practical.

One such method is -greedy, where is a parameter controlling the amount of exploration vs. exploitation. With probability , exploitation is chosen, and the agent chooses the action that it believes has the best long-term effect (ties between actions are broken uniformly at random). Alternatively, with probability , exploration is chosen, and the action is chosen uniformly at random. is usually a fixed parameter but can be adjusted either according to a schedule (making the agent explore progressively less), or adaptively based on heuristics.^{ [6] }

Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which actions are good.

The agent's action selection is modeled as a map called *policy*:

The policy map gives the probability of taking action when in state .^{ [7] }^{:61} There are also non-probabilistic policies.

Value function is defined as the *expected return* starting with state , i.e. , and successively following policy . Hence, roughly speaking, the value function estimates "how good" it is to be in a given state.^{ [7] }^{:60}

where the random variable denotes the **return**, and is defined as the sum of future discounted rewards^{[ clarification needed ]}

where is the reward at step , is the discount-rate^{[ clarification needed ]}.

The algorithm must find a policy with maximum expected return. From the theory of MDPs it is known that, without loss of generality, the search can be restricted to the set of so-called *stationary* policies. A policy is *stationary* if the action-distribution returned by it depends only on the last state visited (from the observation agent's history). The search can be further restricted to *deterministic* stationary policies. A *deterministic stationary* policy deterministically selects actions based on the current state. Since any such policy can be identified with a mapping from the set of states to the set of actions, these policies can be identified with such mappings with no loss of generality.

The brute force approach entails two steps:

- For each possible policy, sample returns while following it
- Choose the policy with the largest expected return

One problem with this is that the number of policies can be large, or even infinite. Another is that variance of the returns may be large, which requires many samples to accurately estimate the return of each policy.

These problems can be ameliorated if we assume some structure and allow samples generated from one policy to influence the estimates made for others. The two main approaches for achieving this are value function estimation and direct policy search.

Value function approaches attempt to find a policy that maximizes the return by maintaining a set of estimates of expected returns for some policy (usually either the "current" [on-policy] or the optimal [off-policy] one).

These methods rely on the theory of MDPs, where optimality is defined in a sense that is stronger than the above one: A policy is called optimal if it achieves the best expected return from *any* initial state (i.e., initial distributions play no role in this definition). Again, an optimal policy can always be found amongst stationary policies.

To define optimality in a formal manner, define the value of a policy by

where stands for the return associated with following from the initial state . Defining as the maximum possible value of , where is allowed to change,

A policy that achieves these optimal values in each state is called *optimal*. Clearly, a policy that is optimal in this strong sense is also optimal in the sense that it maximizes the expected return , since , where is a state randomly sampled from the distribution ^{[ clarification needed ]}.

Although state-values suffice to define optimality, it is useful to define action-values. Given a state , an action and a policy , the action-value of the pair under is defined by

where now stands for the random return associated with first taking action in state and following , thereafter.

The theory of MDPs states that if is an optimal policy, we act optimally (take the optimal action) by choosing the action from with the highest value at each state, . The *action-value function* of such an optimal policy () is called the *optimal action-value function* and is commonly denoted by . In summary, the knowledge of the optimal action-value function alone suffices to know how to act optimally.

Assuming full knowledge of the MDP, the two basic approaches to compute the optimal action-value function are value iteration and policy iteration. Both algorithms compute a sequence of functions () that converge to . Computing these functions involves computing expectations over the whole state-space, which is impractical for all but the smallest (finite) MDPs. In reinforcement learning methods, expectations are approximated by averaging over samples and using function approximation techniques to cope with the need to represent value functions over large state-action spaces.

Monte Carlo methods can be used in an algorithm that mimics policy iteration. Policy iteration consists of two steps: *policy evaluation* and *policy improvement*.

Monte Carlo is used in the policy evaluation step. In this step, given a stationary, deterministic policy , the goal is to compute the function values (or a good approximation to them) for all state-action pairs . Assuming (for simplicity) that the MDP is finite, that sufficient memory is available to accommodate the action-values and that the problem is episodic and after each episode a new one starts from some random initial state. Then, the estimate of the value of a given state-action pair can be computed by averaging the sampled returns that originated from over time. Given sufficient time, this procedure can thus construct a precise estimate of the action-value function . This finishes the description of the policy evaluation step.

In the policy improvement step, the next policy is obtained by computing a *greedy* policy with respect to : Given a state , this new policy returns an action that maximizes . In practice lazy evaluation can defer the computation of the maximizing actions to when they are needed.

Problems with this procedure include:

- The procedure may spend too much time evaluating a suboptimal policy.
- It uses samples inefficiently in that a long trajectory improves the estimate only of the
*single*state-action pair that started the trajectory. - When the returns along the trajectories have
*high variance*, convergence is slow. - It works in
__episodic problems__only; - It works in small, finite MDPs only.

The first problem is corrected by allowing the procedure to change the policy (at some or all states) before the values settle. This too may be problematic as it might prevent convergence. Most current algorithms do this, giving rise to the class of *generalized policy iteration* algorithms. Many *actor critic* methods belong to this category.

The second issue can be corrected by allowing trajectories to contribute to any state-action pair in them. This may also help to some extent with the third problem, although a better solution when returns have high variance is Sutton's temporal difference (TD) methods that are based on the recursive Bellman equation.^{ [8] }^{ [9] } The computation in TD methods can be incremental (when after each transition the memory is changed and the transition is thrown away), or batch (when the transitions are batched and the estimates are computed once based on the batch). Batch methods, such as the least-squares temporal difference method,^{ [10] } may use the information in the samples better, while incremental methods are the only choice when batch methods are infeasible due to their high computational or memory complexity. Some methods try to combine the two approaches. Methods based on temporal differences also overcome the fourth issue.

In order to address the fifth issue, *function approximation methods* are used. *Linear function approximation* starts with a mapping that assigns a finite-dimensional vector to each state-action pair. Then, the action values of a state-action pair are obtained by linearly combining the components of with some *weights*:

The algorithms then adjust the weights, instead of adjusting the values associated with the individual state-action pairs. Methods based on ideas from nonparametric statistics (which can be seen to construct their own features) have been explored.

Value iteration can also be used as a starting point, giving rise to the Q-learning algorithm and its many variants.^{ [11] }

The problem with using action-values is that they may need highly precise estimates of the competing action values that can be hard to obtain when the returns are noisy. Though this problem is mitigated to some extent by temporal difference methods. Using the so-called compatible function approximation method compromises generality and efficiency. Another problem specific to TD comes from their reliance on the recursive Bellman equation. Most TD methods have a so-called parameter that can continuously interpolate between Monte Carlo methods that do not rely on the Bellman equations and the basic TD methods that rely entirely on the Bellman equations. This can be effective in palliating this issue.

An alternative method is to search directly in (some subset of) the policy space, in which case the problem becomes a case of stochastic optimization. The two approaches available are gradient-based and gradient-free methods.

Gradient-based methods (*policy gradient methods*) start with a mapping from a finite-dimensional (parameter) space to the space of policies: given the parameter vector , let denote the policy associated to . Defining the performance function by

under mild conditions this function will be differentiable as a function of the parameter vector . If the gradient of was known, one could use gradient ascent. Since an analytic expression for the gradient is not available, only a noisy estimate is available. Such an estimate can be constructed in many ways, giving rise to algorithms such as Williams' REINFORCE method^{ [12] } (which is known as the likelihood ratio method in the simulation-based optimization literature).^{ [13] } Policy search methods have been used in the robotics context.^{ [14] } Many policy search methods may get stuck in local optima (as they are based on local search).

A large class of methods avoids relying on gradient information. These include simulated annealing, cross-entropy search or methods of evolutionary computation. Many gradient-free methods can achieve (in theory and in the limit) a global optimum.

Policy search methods may converge slowly given noisy data. For example, this happens in episodic problems when the trajectories are long and the variance of the returns is large. Value-function based methods that rely on temporal differences might help in this case. In recent years, *actor–critic methods* have been proposed and performed well on various problems.^{ [15] }

Both the asymptotic and finite-sample behavior of most algorithms is well understood. Algorithms with provably good online performance (addressing the exploration issue) are known.

Efficient exploration of large MDPs is largely unexplored (except for the case of bandit problems).^{[ clarification needed ]} Although finite-time performance bounds appeared for many algorithms, these bounds are expected to be rather loose and thus more work is needed to better understand the relative advantages and limitations.

For incremental algorithms, asymptotic convergence issues have been settled. Temporal-difference-based algorithms converge under a wider set of conditions than was previously possible (for example, when used with arbitrary, smooth function approximation).

Research topics include

- adaptive methods that work with fewer (or no) parameters under a large number of conditions
- addressing the exploration problem in large MDPs
- large-scale empirical evaluations
- learning and acting under partial information (e.g., using predictive state representation)
- modular and hierarchical reinforcement learning
- improving existing value-function and policy search methods
- algorithms that work well with large (or continuous) action spaces
- transfer learning
- lifelong learning
- efficient sample-based planning (e.g., based on Monte Carlo tree search).
- bug detection in software projects
^{ [16] }

Multiagent or distributed reinforcement learning is a topic of interest. Applications are expanding.^{ [17] }

- Actor-critic reinforcement learning

Reinforcement learning algorithms such as TD learning are under investigation as a model for dopamine-based learning in the brain. In this model, the dopaminergic projections from the substantia nigra to the basal ganglia function as the prediction error. Reinforcement learning has been used as a part of the model for human skill learning, especially in relation to the interaction between implicit and explicit learning in skill acquisition (the first publication on this application was in 1995–1996).^{ [18] }

Algorithm | Description | Model | Policy | Action Space | State Space | Operator |
---|---|---|---|---|---|---|

Monte Carlo | Every visit to Monte Carlo | Model-Free | Off-policy | Discrete | Discrete | Sample-means |

Q-learning | State–action–reward–state | Model-Free | Off-policy | Discrete | Discrete | Q-value |

SARSA | State–action–reward–state–action | Model-Free | On-policy | Discrete | Discrete | Q-value |

Q-learning - Lambda | State–action–reward–state with eligibility traces | Model-Free | Off-policy | Discrete | Discrete | Q-value |

SARSA - Lambda | State–action–reward–state–action with eligibility traces | Model-Free | On-policy | Discrete | Discrete | Q-value |

DQN | Deep Q Network | Model-Free | Off-policy | Discrete | Continuous | Q-value |

DDPG | Deep Deterministic Policy Gradient | Model-Free | Off-policy | Continuous | Continuous | Q-value |

A3C | Asynchronous Advantage Actor-Critic Algorithm | Model-Free | On-policy | Continuous | Continuous | Advantage |

NAF | Q-Learning with Normalized Advantage Functions | Model-Free | Off-policy | Continuous | Continuous | Advantage |

TRPO | Trust Region Policy Optimization | Model-Free | On-policy | Continuous | Continuous | Advantage |

PPO | Proximal Policy Optimization | Model-Free | On-policy | Continuous | Continuous | Advantage |

TD3 | Twin Delayed Deep Deterministic Policy Gradient | Model-Free | Off-policy | Continuous | Continuous | Q-value |

SAC | Soft Actor-Critic | Model-Free | Off-policy | Continuous | Continuous | Advantage |

This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space.^{ [19] } The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning.^{ [20] }

In inverse reinforcement learning (IRL), no reward function is given. Instead, the reward function is inferred given an observed behavior from an expert. The idea is to mimic observed behavior, which is often optimal or close to optimal.^{ [21] }

In apprenticeship learning, an expert demonstrates the target behavior. The system tries to recover the policy via observation.

**Mathematical optimization** or **mathematical programming** is the selection of a best element from some set of available alternatives. Optimization problems of sorts arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

**Stochastic gradient descent** is an iterative method for optimizing an objective function with suitable smoothness properties. It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient by an estimate thereof. Especially in big data applications this reduces the computational burden, achieving faster iterations in trade for a slightly lower convergence rate.

**Temporal difference** (**TD**) **learning** refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods.

A **Bellman equation**, named after Richard E. Bellman, is a necessary condition for optimality associated with the mathematical optimization method known as dynamic programming. It writes the "value" of a decision problem at a certain point in time in terms of the payoff from some initial choices and the "value" of the remaining decision problem that results from those initial choices. This breaks a dynamic optimization problem into a sequence of simpler subproblems, as Bellman's “principle of optimality” prescribes.

** Q-learning** is a model-free reinforcement learning algorithm. The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances. It does not require a model of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.

In probability theory, the **multi-armed bandit problem** is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice. This is a classic reinforcement learning problem that exemplifies the exploration-exploitation tradeoff dilemma. The name comes from imagining a gambler at a row of slot machines, who has to decide which machines to play, how many times to play each machine and in which order to play them, and whether to continue with the current machine or try a different machine. The multi-armed bandit problem also falls into the broad category of stochastic scheduling.

A **partially observable Markov decision process** (**POMDP**) is a generalization of a Markov decision process (MDP). A POMDP models an agent decision process in which it is assumed that the system dynamics are determined by an MDP, but the agent cannot directly observe the underlying state. Instead, it must maintain a probability distribution over the set of possible states, based on a set of observations and observation probabilities, and the underlying MDP.

**Stochastic approximation** methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive update rules of stochastic approximation methods can be used, among other things, for solving linear systems when the collected data is corrupted by noise, or for approximating extreme values of functions which cannot be computed directly, but only estimated via noisy observations.

The **Gittins index** is a measure of the reward that can be achieved through a given stochastic process with certain properties, namely: the process has an ultimate termination state and evolves with an option, at each intermediate state, of terminating. Upon terminating at a given state, the reward achieved is the sum of the probabilistic expected rewards associated with every state from the actual terminating state to the ultimate terminal state, inclusive. The index is a real scalar.

**State–action–reward–state–action** (**SARSA**) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning. It was proposed by Rummery and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only mentioned as a footnote.

In computer science, **online machine learning** is a method of machine learning in which data becomes available in a sequential order and is used to update our best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once. Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need of out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction. Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches.

**AIXI**['ai̯k͡siː] is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book *Universal Artificial Intelligence*.

**Natural evolution strategies** (**NES**) are a family of numerical optimization algorithms for black box problems. Similar in spirit to evolution strategies, they iteratively update the (continuous) parameters of a *search distribution* by following the natural gradient towards higher expected fitness.

**Mountain Car**, a standard testing domain in Reinforcement Learning, is a problem in which an under-powered car must drive up a steep hill. Since gravity is stronger than the car's engine, even at full throttle, the car cannot simply accelerate up the steep slope. The car is situated in a valley and must learn to leverage potential energy by driving up the opposite hill before the car is able to make it to the goal at the top of the rightmost hill. The domain has been used as a test bed in various Reinforcement Learning papers.

In applied mathematics, **proto-value functions (PVFs)** are automatically learned basis functions that are useful in approximating task-specific value functions, providing a compact representation of the powers of transition matrices. They provide a novel framework for solving the credit assignment problem. The framework introduces a novel approach to solving Markov decision processes (MDP) and reinforcement learning problems, using multiscale spectral and manifold learning methods. Proto-value functions are generated by spectral analysis of a graph, using the graph Laplacian.

**Automatic basis function construction** is the mathematical method of looking for a set of task-independent basis functions that map the state space to a lower-dimensional embedding, while still representing the value function accurately. Automatic basis construction is independent of prior knowledge of the domain, which allows it to perform well where expert-constructed basis functions are difficult or impossible to create.

**Thompson sampling**, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists in choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

**Multiple kernel learning** refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source.

In computational statistics, the **Metropolis-adjusted Langevin algorithm (MALA)** or **Langevin Monte Carlo (LMC)** is a Markov chain Monte Carlo (MCMC) method for obtaining random samples – sequences of random observations – from a probability distribution for which direct sampling is difficult. As the name suggests, MALA uses a combination of two mechanisms to generate the states of a random walk that has the target probability distribution as an invariant measure:

- ↑ Kaelbling, Leslie P.; Littman, Michael L.; Moore, Andrew W. (1996). "Reinforcement Learning: A Survey".
*Journal of Artificial Intelligence Research*.**4**: 237–285. arXiv: cs/9605103 . doi:10.1613/jair.301. Archived from the original on 2001-11-20. - ↑ van Otterlo, M.; Wiering, M. (2012).
*Reinforcement learning and markov decision processes*.*Reinforcement Learning*. Adaptation, Learning, and Optimization.**12**. pp. 3–42. doi:10.1007/978-3-642-27645-3_1. ISBN 978-3-642-27644-6. - ↑ Sutton & Barto, Chapter 11.
- ↑ Gosavi, Abhijit (2003).
*Simulation-based Optimization: Parametric Optimization Techniques and Reinforcement*. Operations Research/Computer Science Interfaces Series. Springer. ISBN 978-1-4020-7454-7. - ↑ Burnetas, Apostolos N.; Katehakis, Michael N. (1997), "Optimal adaptive policies for Markov Decision Processes",
*Mathematics of Operations Research*,**22**: 222–255, doi:10.1287/moor.22.1.222 - ↑ Tokic, Michel; Palm, Günther (2011), "Value-Difference Based Exploration: Adaptive Control Between Epsilon-Greedy and Softmax" (PDF),
*KI 2011: Advances in Artificial Intelligence*, Lecture Notes in Computer Science,**7006**, Springer, pp. 335–346, ISBN 978-3-642-24455-1 - 1 2
*Reinforcement learning: An introduction*(PDF). - ↑ Sutton, Richard S. (1984).
*Temporal Credit Assignment in Reinforcement Learning*(PhD thesis). University of Massachusetts, Amherst, MA. - ↑ Sutton & Barto 1998, §6. Temporal-Difference Learning.
- ↑ Bradtke, Steven J.; Barto, Andrew G. (1996). "Learning to predict by the method of temporal differences".
*Machine Learning*.**22**: 33–57. CiteSeerX 10.1.1.143.857 . doi:10.1023/A:1018056104778. - ↑ Watkins, Christopher J.C.H. (1989).
*Learning from Delayed Rewards*(PDF) (PhD thesis). King’s College, Cambridge, UK. - ↑ Williams, Ronald J. (1987). "A class of gradient-estimating algorithms for reinforcement learning in neural networks".
*Proceedings of the IEEE First International Conference on Neural Networks*. CiteSeerX 10.1.1.129.8871 . - ↑ Peters, Jan; Vijayakumar, Sethu; Schaal, Stefan (2003). "Reinforcement Learning for Humanoid Robotics" (PDF).
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*A Survey on Policy Search for Robotics*. Foundations and Trends in Robotics.**2**. NOW Publishers. pp. 1–142. hdl:10044/1/12051. - ↑ Juliani, Arthur (2016-12-17). "Simple Reinforcement Learning with Tensorflow Part 8: Asynchronous Actor-Critic Agents (A3C)".
*Medium*. Retrieved 2018-02-22. - ↑ "On the Use of Reinforcement Learning for Testing Game Mechanics : ACM - Computers in Entertainment".
*cie.acm.org*. Retrieved 2018-11-27. - ↑ "Reinforcement Learning / Successes of Reinforcement Learning".
*umichrl.pbworks.com*. Retrieved 2017-08-06. - ↑ Archived 2017-04-26 at the Wayback Machine
- ↑ Francois-Lavet, Vincent; et al. (2018). "An Introduction to Deep Reinforcement Learning".
*Foundations and Trends in Machine Learning*.**11**(3–4): 219–354. arXiv: 1811.12560 . doi:10.1561/2200000071. - ↑ Mnih, Volodymyr; et al. (2015). "Human-level control through deep reinforcement learning".
*Nature*.**518**(7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. - ↑ Ng, A. Y.; Russell, S. J. (2000). "Algorithms for Inverse Reinforcement Learning" (PDF).
*Proceeding ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning*. pp. 663–670. ISBN 1-55860-707-2.

- Auer, Peter; Jaksch, Thomas; Ortner, Ronald (2010). "Near-optimal regret bounds for reinforcement learning".
*Journal of Machine Learning Research*.**11**: 1563–1600. - Busoniu, Lucian; Babuska, Robert; De Schutter, Bart; Ernst, Damien (2010).
*Reinforcement Learning and Dynamic Programming using Function Approximators*. Taylor & Francis CRC Press. ISBN 978-1-4398-2108-4. - François-Lavet, Vincent; Henderson, Peter; Islam, Riashat; Bellemare, Marc G.; Pineau, Joelle (2018). "An Introduction to Deep Reinforcement Learning".
*Foundations and Trends in Machine Learning*.**11**(3–4): 219–354. arXiv: 1811.12560 . doi:10.1561/2200000071. - Powell, Warren (2007).
*Approximate dynamic programming: solving the curses of dimensionality*. Wiley-Interscience. ISBN 978-0-470-17155-4. - Sutton, Richard S.; Barto, Andrew G. (1998).
*Reinforcement Learning: An Introduction*. MIT Press. ISBN 978-0-262-19398-6. - Sutton, Richard S. (1988). "Learning to predict by the method of temporal differences".
*Machine Learning*.**3**: 9–44. doi:10.1007/BF00115009. - Szita, Istvan; Szepesvari, Csaba (2010). "Model-based Reinforcement Learning with Nearly Tight Exploration Complexity Bounds" (PDF).
*ICML 2010*. Omnipress. pp. 1031–1038. Archived from the original (PDF) on 2010-07-14.

- Reinforcement Learning Repository
- Reinforcement Learning and Artificial Intelligence (RLAI, Rich Sutton's lab at the University of Alberta)
- A Beginner's Guide to Deep Reinforcement Learning
- Autonomous Learning Laboratory (ALL, Andrew Barto's lab at the University of Massachusetts Amherst)
- Hybrid reinforcement learning
- Real-world reinforcement learning experiments at Delft University of Technology
- Stanford University Andrew Ng Lecture on Reinforcement Learning
- Dissecting Reinforcement Learning Series of blog post on RL with Python code

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