The goal of rl with function approximation is then to learn the best values for this parameter vector. In this work, we study value function approximation in reinforcement learning rl problems with high dimensional state or action spaces via a generalized version of representation policy. Making sense of the bias variance tradeoff in deep reinforcement learning. Due to the use of a more expressive representation language to represent states, actions and q functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. Issues in using function approximation for reinforcement learning. I, michael mitchley, declare that this thesis titled adaptive value function approximation in reinforcement learning using wavelets and the work presented in it are my own. Value function approximation alan fern based in part on slides by daniel weld. Sparse value function approximation for reinforcement learning. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. 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.
Here we instead take a function approximation approach to reinforcement learning for this same problem. Sampleefficient evolutionary function approximation for. Pdf value function approximation in graphbased reinforcement. Reinforcement learning rl in continuous state spaces requires function approximation. Theory and algorithms working draft markov decision processes alekh agarwal, nan jiang, sham m. Find file copy path reinforcement learning fa q learning with value function approximation solution. Cbr is one of the techniques that can be applied to the task of approximating a function over highdimensional, continuous spaces. Combining reinforcement learning with function approximation techniques allows the agent to generalize and hence handle large even in nite number of states. We identify two aspects of reinforcement learning that make the function approximation process hard. The application of reinforcement learning to problems with continuous domains requires representing the value function by means of function approximation.
Function approximation finding optimal v a knowledge of value for all states. Value function approximation in reinforcement learning using. This drawback is currently handled by manual filtering of sam. Instead of learning an approximation of the underlying value function and basing the policy on a direct estimate of. Cbr for state value function approximation in reinforcement. For brevity, we focus on the l 0 case, for which the approximate value function solution is. Qlearning with linear function approximation gaips. Issues in using function approximation for reinforcement. 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. Large applications of reinforcement learning rl require the use of generalizing func tion approximators such neural networks, decisiontrees, or instancebased methods. 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. 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.
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. Oct 31, 2016 going deeper into reinforcement learning. A tutorial on linear function approximators for dynamic. Symmetry learning for function approximation in reinforcement learning anuj mahajanyand theja tulabandhulaz yconduent labs india. Symmetry learning for function approximation in reinforcement. Pdf policy gradient methods for reinforcement learning with. 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. Like other td methods, qlearning attempts to learn a value function that maps stateaction pairs to values. Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and deter mining a policy from it has. An alternative method for reinforcement learning that bypasses these limitations is a policygradient approach. An obvious method for combining them with function approximation systems, which is called the direct algorithm here, can be. Policy gradient methods for reinforcement learning with function approximation richard s. 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.
Making sense of the bias variance tradeoff in deep. Value function approximation by stochastic gradient descent. In principle, evolutionary function approximation can be used with any of them. The dominant approach for the last decade has been the valuefunction approach, in which all function approximation effort goes into estimating a value function, with. The value function approximation structure for today closely follows much of david silvers lecture 6.
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. Pdf reinforcement learning and function approximation. An analysis of reinforcement learning with function approximation francisco s. Competitive function approximation for reinforcement learning. Policy gradient methods for reinforcement learning with.
Now consider how to do value function approximation for prediction evaluation policy evaluation without a model note. Reinforcement learning in continuous state spaces requires function approximation. Some approximate solution methods rely on valuebased reinforcement learn. Kernelized value function approximation for reinforcement learning to derive a kernelized version of lstd. This l 1 regularization approach was rst applied to temporal.
Function approximation in reinforcement learning towards. Algorithms such as q learning or value iteration are guaranteed to converge to the optimal answer when used with a lookup table. Reinforcement learning techniquesaddress theproblemof learningto select actionsin unknown,dynamic environments. 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. In the nonlinear function approximator we will redefine once again the state and action value function v and q such as. In scaling reinforcement learning to problems with large numbers of states andor actions, the representation of the value function becomes critical. Pdf in this work, we study value function approximation in reinforcement learning rl problems with high dimensional state or action spaces.
The value function of a state is the sum of the reward to be received when the agent moves to the next state and is affected by the current agents policy. Kernelized value function approximation for reinforcement. We present a decision tree based approach to function approximation in reinforcement learning. 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. Value function approximation in reinforcement learning. In section 6, we provide an algorithm that combines the knowledge of all previous sections.
An analysis of linear models, linear valuefunction. Reinforcement learning lecture value function approximation. Understanding qlearning and linear function approximation. An analysis of linear models, linear value function approximation, and feature selection for reinforcement learning 2. Evolutionary function approximation for reinforcement learning td method. Policy gradient methods for reinforcement learning with function. Reinforcement learning with function approximation richard s. We obtain similar learning accuracies, with much better running times, allowing us to consider much larger problem sizes. Reinforcement learning with function approximation converges to. 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. There are too many states andor actions to store in memory. Decision tree function approximation in reinforcement learning. 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.