nt in Visual Studio .NET

Generation QR Code 2d barcode in Visual Studio .NET nt
nt
Quick Response Code Recognizer In .NET Framework
Using Barcode Control SDK for .NET framework Control to generate, create, read, scan barcode image in Visual Studio .NET applications.
r(t)
Make Quick Response Code In .NET
Using Barcode creator for .NET Control to generate, create QR Code ISO/IEC18004 image in VS .NET applications.
(63)
Recognize QR-Code In Visual Studio .NET
Using Barcode reader for Visual Studio .NET Control to read, scan read, scan image in VS .NET applications.
A problem with this model is that it is not possible to distinguish between a policy that gains a large amount of reward in the initial phases, and a policy where the largest gain is obtained in the later phases In order to nd an optimal policy, , it is necessary to nd an optimal value function A candidate optimal value function is [432], V (s) = max R(s, a) + T (s, a, s )V (s ) , s S (64) a A
Generating Bar Code In Visual Studio .NET
Using Barcode generator for .NET framework Control to generate, create barcode image in .NET framework applications.
s S
Recognize Barcode In .NET Framework
Using Barcode decoder for .NET Control to read, scan read, scan image in .NET framework applications.
where A is the set of all possible actions, S is the set of environmental states, R(s, a) is the reward function, and T (s, a, s ) is the transition function Equation (64) states that the value of a state, s, is the expected instantaneous reward, R(s, a), for action a plus the expected discounted value of the next state, using the best possible action From the above, a clear de nition of the model in terms of the transition function, T , and the reward function, R, is required A number of algorithms have been developed for such RL problems The reader is referred to [432, 824] for a summary of these methods Of more interest to this chapter are model-free learning methods, as described in the next section
Generating QR Code JIS X 0510 In Visual C#
Using Barcode encoder for Visual Studio .NET Control to generate, create Quick Response Code image in .NET framework applications.
6 Reinforcement Learning
Paint QR In Visual Studio .NET
Using Barcode creator for ASP.NET Control to generate, create Quick Response Code image in ASP.NET applications.
Model-Free Reinforcement Learning Model
Making QR Code ISO/IEC18004 In VB.NET
Using Barcode printer for Visual Studio .NET Control to generate, create QR Code image in VS .NET applications.
This section considers model-free RL methods, where the objective is to obtain an optimal policy without a model of the environment This section reviews two approaches, namely temporal di erence (TD) learning (in Section 621) and Q-learning (in Section 622)
Data Matrix 2d Barcode Generation In .NET
Using Barcode creation for Visual Studio .NET Control to generate, create Data Matrix image in Visual Studio .NET applications.
Temporal Di erence Learning
UPC Code Encoder In VS .NET
Using Barcode printer for Visual Studio .NET Control to generate, create UPC-A Supplement 5 image in .NET framework applications.
Temporal di erence (TD) learning [824] learns the value policy using the update rule, V (s) = V (s) + (r + V (s ) V (s)) (65)
Making GS1 - 13 In .NET Framework
Using Barcode generator for VS .NET Control to generate, create GTIN - 13 image in VS .NET applications.
where is a learning rate, r is the immediate reward, is the discount factor, s is the current state, and s is a future state Based on equation (65), whenever a state, s, is visited, its estimated value is updated to be closer to r + V (s ) The above model is referred to as TD(0), where only one future step is considered The TD method has been generalized to TD( ) strategies [825], where [0, 1] is a weighting on the relevance of recent temporal di erences of previous predictions For TD( ), the value function is learned using V (u) = V (u) + (r + V (s ) V (s))e(u) (66)
European Article Number 8 Maker In VS .NET
Using Barcode generator for Visual Studio .NET Control to generate, create GTIN - 8 image in VS .NET applications.
where e(u) is the eligibility of state u The eligibility of a state is the degree to which the state has been visited in the recent past, computed as
Generating EAN 13 In Java
Using Barcode encoder for Java Control to generate, create UPC - 13 image in Java applications.
e(s) = where s,st =
EAN-13 Supplement 5 Encoder In VB.NET
Using Barcode generator for .NET framework Control to generate, create EAN / UCC - 13 image in .NET framework applications.
( )t t s,st
Bar Code Creation In Java
Using Barcode generation for Java Control to generate, create bar code image in Java applications.
t =1
Recognizing EAN-13 In .NET Framework
Using Barcode recognizer for VS .NET Control to read, scan read, scan image in .NET framework applications.
(67)
UPC Code Creation In VS .NET
Using Barcode creation for ASP.NET Control to generate, create UPCA image in ASP.NET applications.
1 s = st 0 otherwise
Code 128 Code Set A Printer In Java
Using Barcode encoder for Java Control to generate, create USS Code 128 image in Java applications.
(68)
Code 39 Creation In VB.NET
Using Barcode maker for VS .NET Control to generate, create Code 39 Extended image in .NET framework applications.
The update in equation (66) is applied to every state, according to its eligibility, and not just the previous state as for TD(0)
Bar Code Recognizer In .NET Framework
Using Barcode decoder for VS .NET Control to read, scan read, scan image in .NET framework applications.
Q-Learning
In Q-learning [891], the task is to learn the expected discounted reinforcement values, Q(s, a), of taking action a in state s, then continuing by always choosing actions optimally To relate Q-values to the value function, note that V (s) = max Q (s, a)
(69)
where V (s) is the value of s assuming that the best action is taken initially
63 Neural Networks and Reinforcement Learning The Q-learning rule is given as Q(s, a) = Q(s, a) + (r + max Q(s , a ) Q(s, a))
a A
(610)
The agent then takes the action with the highest Q-value
Neural Networks and Reinforcement Learning
Neural networks and reinforcement learning have been combined in a number of ways One approach of combining these models is to use a NN as an approximator of the value function used to predict future reward [162, 432] Another approach uses RL to adjust weights Both these approaches are discussed in this section As already indicated, the LVQ-II (refer to Section 51) implements a form of RL Weights of the winning output unit are positively updated only if that output unit provided the correct response for the corresponding input pattern If not, weights are penalized through adjustment away from that input pattern Other approaches to use RL for NN training include RPROP (refer to Section 631), and gradient descent on the expected reward (refer to Section 632) Connectionist Q-learning is used to approximate the value function (refer to Section 633)