Machine Learning - DISCo

CHAPTER 13 REINFORCEMENT LEARNJNG 375
This recursive definition of Q provides the basis for algorithms that iteratively
approximate Q (Watkins 1989). To describe the algorithm, we will use
the symbol Q to refer to the learner's estimate, or hypothesis, of the actual Q
function. In this algorithm the learner represents its hypothesis Q by a large table
with a separate entry for each state-action pair. The table entry for the pair (s, a)
stores the value for ~(s, a)-the learner's current hypothesis about the actual
but unknown value Q(s, a). The table can be initially filled with random values
(though it is easier to understand the algorithm if one assumes initial values of
zero). The agent repeatedly observes its current state s, chooses some action a,
executes this action, then observes the resulting reward r = r(s, a) and the new
state s' = 6(s, a). It then updates the table entry for ~(s, a) following each such
transition, according to the rule:
Q(S, a) t r + y max &(st, a')
a'
(13.7)
Note this training rule uses the agent's current Q values for the new state
s' to refine its estimate of ~(s, a) for the previous state s. This training rule
is motivated by Equation (13.6), although the training rule concerns the agent's
approximation Q, whereas Equation (13.6) applies to the actual Q function. Note
although Equation (13.6) describes Q in terms of the functions 6(s, a) and r(s, a),
the agent does not need to know these general functions to apply the training
rule of Equation (13.7). Instead it executes the action in its environment and
then observes the resulting new state s' and reward r. Thus, it can be viewed as
sampling these functions at the current values of s and a.
The above Q learning algorithm for deterministic Markov decision processes
is described more precisely in Table 13.1. Using this algorithm the agent's estimate
Q converges in the limit to the actual Q function, provided the system can be
modeled as a deterministic Markov decision process, the reward function r is
Q learning algorithm
For each s, a initialize the table entry ~(s, a) to zero.
Observe the current state s
Do forever:
Select an action a and execute it
Receive immediate reward r
Observe the new state s'
Update the table entry for ~ ( s a) , as follows:
~(s,a) c r
+ ymax&(s',af)
a'
S CS'
TABLE 13.1
Q learning algorithm, assuming deterministic rewards and actions. The discount factor y may be any
constant such that 0 5 y < 1.