Value function approximation reinforcement learning pdf

Value function approximation in reinforcement learning using. Making sense of the bias variance tradeoff in deep. Evolutionary function approximation for reinforcement learning basis functions. Little, however, is understood about the theoretical properties of such. Most work in this area focuses on linear function approximation, where the value function is represented as a weighted linear sum of a set of features known as basis functions computed from the state variables. Implementation of reinforcement learning algorithms. In section 6, we provide an algorithm that combines the knowledge of all previous sections. In most realworld reinforcement learning tasks, td methods require a function approximator to represent the value function. Value function approximation in reinforcement learning. Although there are convergent online algorithms such as td 1 for learning the parameters of a linear approximation to the value function of a markov.

Reinforcement learning rl in continuous state spaces requires function approximation. Issues in using function approximation for reinforcement. Any system that enumerates separate value functions and learns each individually like the horde is hampered in its scalability, as it cannot take advantage of any shared structure unless the demons share parameters. It is generally used to deal with continuous state spaces, and to allow generalization between sim. Value function approximation in graphbased reinforcement. Approximation in value space multistep lookahead approximation in.

For a given value function v, and a given state x, the bellman residual is defined to be the. Kernelized value function approximation for reinforcement. Novel function approximation techniques for largescale. Linear value functions in cases where the value function cannot be represented exactly, it is common to use some form of parametric value function approximation, such as a linear combination of features or basis functions. Evolutionary function approximation for reinforcement learning. Value function approximation in graphbased reinforcement learning overall goals to study value function approximation in reinforcement learning problems with high dimensional state or action spaces to depart from the smoothness assumption for the state value function to highlight the importance of features learning for an improved. Relational reinforcement learning rrl combines traditional reinforcement learning rl with a strong emphasis on a relational rather than attribute value representation. Now, instead of storing v values, we will update parameters using. In this study, we call this function the dsilu and we propose it as a competitive alternative to the sigmoid function in neural network function approximation in reinforcement learning.

Fast feature selection for linear value function approximation. An analysis of linear models, linear value function approximation, and feature selection for reinforcement learning 2. Decision tree function approximation in reinforcement. Value function approximation by stochastic gradient descent. Universal value function approximators demons that summarizes a whole class of predictions in a single object. We present a decision tree based approach to function approximation in reinforcement learning. Evolutionary function approximation for reinforcement. Edu department of computer science, duke university, durham, nc 27708 usa abstract a recent surge in research in kernelized approaches to reinforcement learning has sought to bring the bene. However, using function approximators requires manually making crucial representational decisions. In this paper we explore an alternative approach in which the policy is explicitly represented by its own function approximator, independent of the value function, and is updated according to the. Managing uncertainty within value function approximation in. Policy gradient methods for reinforcement learning with. An analysis of linear models, linear valuefunction.

Competitive function approximation for reinforcement learning. Perhaps the simplest form of reinforcement learning problem is the task of learning the value function for a markov chain, which is a degenerate mdp for which there is only one possible action to choose from in each state. Here we instead take a function approximation approach to reinforcement learning for this same problem. Tesauro 1994 and sophisticated methods for optimizing their representations gruau et al. May 21, 2019 the main drawback of linear function approximation compared to nonlinear function approximation, such as the neural network, is the need for good handpicked features, which may require domain knowledge. Reinforcement learning in continuous state spaces requires function approximation. Managing uncertainty within value function approximation. Pdf policy gradient methods for reinforcement learning.

Reinforcement learning lecture value function approximation. Function approximation finding optimal v a knowledge of value for all states. In principle, evolutionary function approximation can be used with any of them. This drawback is currently handled by manual filtering of sam.

Reinforcement learning with function approximation converges to. An analysis of linear models, linear valuefunction approximation, and feature selection for reinforcement learning. We obtain similar learning accuracies, with much better running times, allowing us to consider much larger problem sizes. There are too many states andor actions to store in memory. Valuefunction approximation vfa is a technique in reinforcement learning rl where the tab ular representation of the value function is replaced with a generalpurpose function approximator. The generalized advantage estimate gae, introduced by john. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Pdf policy gradient methods for reinforcement learning with. Relational reinforcement learning rrl combines traditional reinforcement learning rl with a strong emphasis on a relational rather than attributevalue representation. Nonstationarity is the result of the bootstrapping nature of dynamic programming where the value function is estimated using its current approximation.

For additional reading please see sb 2018 sections 9. In multiresolution analysis, a key ingredient is re. Reinforcement learning can be used to solve large problems, e. Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and deter mining a policy from it has so far proven theoretically intractable. Reinforcement learning rl is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Most work in this area focuses on linear function approximation, where the value function is represented as a weighted linear sum of a set of features also known as basis functions computed from the available state variables.

Instead of learning an approximation of the underlying value function and basing the policy on a direct estimate of. Combining reinforcement learning with function approximation techniques allows the agent to generalize and hence handle large even in nite number of states. The goal of rl with function approximation is then to learn the best values for this parameter vector. In scaling reinforcement learning to problems with large numbers of states andor actions, the representation of the value function becomes critical. Sigmoidweighted linear units for neural network function. Sparse value function approximation for reinforcement learning. Ebrahim momoniat athesispresentedforthedegreeof doctor of philosophy school of computational and applied mathematics university of the witwatersrand, johannesburg south africa july, 2015. Most work in this area fo cuses on linear function approximation, where the value function is represented as a weighted linear sum of a set of features known as basis functions computed from the state variables. Explicit manifold representations for valuefunction. First assume we could query any state s and an oracle would return the true value for v. Some approximate solution methods rely on valuebased reinforcement learn. We consider a reinforcement learning problem formulated as a markov decision process mdp with states s, actions a, transition probabilities p. The value function approximation structure for today closely follows much of david silvers lecture 6.

Since value function approximation in reinforcement learning is also an approximation problem, it is natural to consider leveraging this technique. This paper investigates evolutionary function approximation, a novel approach to automatically selecting function. Winter 2018 the value function approximation structure for today closely follows much of david silvers lecture 6. Reinforcement learning and optimal control by dimitri p. Decision tree function approximation in reinforcement learning. Oct 31, 2016 value iteration with linear function approximation, a relatively easytounderstand algorithm that should serve as your first choice if you need to scale up tabular value iteration for a simple reinforcement learning problem. Reinforcement learning rl in continuous state spaces re quires function approximation. Reinforcement learning techniquesaddress theproblemof learningto select actionsin unknown,dynamic environments. Issues in using function approximation for reinforcement learning. Managing uncertainty within value function approximation in reinforcement learning matthieu geist olivier pietquin ims research group supelec, metz, france matthieu. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as arti. Qlearning with linear function approximation gaips.

In this paper, we analyze the convergence of qlearning with linear func. Winter 2020 the value function approximation structure for today closely follows much of david silvers lecture 6. Reinforcement learning with function approximation richard s. Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. The activation of the dsilu is computed by the derivative of the silu see right panel in. In reinforcement learning, the interactions between the agent and the environment are often described by a markov decision process mdp puterman, 1994, speci. Distributional reinforcement learning with linear function. Value function approximation for policy evaluation with an oracle. Vx 4 for a system with a finite number of states, the optimal value function is the unique function that satisfies the bellman equation. Value function approximation emma brunskill cs234 reinforcement learning. Sampleefficient evolutionary function approximation for.

Function approximation in reinforcement learning towards. This l 1 regularization approach was rst applied to temporal. Linear value functions in cases where the value function cannot be represented exactly, it is common to use some form of parametric valuefunction approximation, such as a linear combination of features or basis functions. Sparse value function approximation for reinforcement. An analysis of linear models, linear valuefunction approximation, and feature selection for reinforcement learning 2. Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. In the nonlinear function approximator we will redefine once again the state and action value function v and q such as. Competitive function approximation for reinforcement. Exercises and solutions to accompany suttons book and david silvers course. An alternative method for reinforcement learning that bypasses these limitations is a policygradient approach. Users who have contributed to this file 376 lines 376 sloc 187 kb raw blame history. Q learning with linear function approximation, which approximates values with a linear function, i. Create parametric thus learnable functions to approximate the value function. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning reinforcement learning differs from supervised learning in not needing.

It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as. Finally, employing neural networks is feasible because they have previously succeeded as td function approximators crites and barto 1998. Adaptive value function approximation in reinforcement. Despite many algorithmic advances, our theoretical understanding of practical distributional reinforcement learning methods remains limited. Competitive function approximation for reinforcement learning iri. Novel function approximation techniques for largescale reinforcement learning a dissertation by cheng wu to the graduate school of engineering. 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 stateaction spaces. For example, the value function can be applied to directly smooth the reinforcement signal obtained from a series of trajectories. Value function approximation introduction value function approximation so far we have represented value function by a lookup table every state s has an entry vs or every stateaction pair s. Adaptive value function approximation in reinforcement learning using wavelets michael mitchley supervised by prof. One method for obtaining sparse linear approximations is the inclusion in the objective function of a penalty on the sum of the absolute values of the approximation weights.

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