This document discusses reinforcement learning, an approach to machine learning where an agent learns behaviors through trial and error interactions with its environment. The agent receives positive or negative feedback based on its actions, allowing it to maximize rewards. Specifically: 1) In reinforcement learning, an agent performs actions in an environment and receives feedback in the form of rewards or punishments to learn behaviors without a teacher directly telling it what to do. 2) The goal is for the agent to learn a policy to map states to actions that will maximize total rewards. It must figure out which of its past actions led to rewards through the "credit assignment problem." 3) Reinforcement learning has been applied to problems like game playing, robot control

1. Reinforcement Learning
Russell and Norvig: ch 21
CMSC 671 – Fall 2005
Slides from Jean-Claude
Latombe and Lise Getoor

2. Reinforcement Learning
Supervised (inductive) learning is the simplest and
most studied type of learning
How can an agent learn behaviors when it doesn’t
have a teacher to tell it how to perform?
 The agent has a task to perform
 It takes some actions in the world
 At some later point, it gets feedback telling it how well it did
on performing the task
 The agent performs the same task over and over again
This problem is called reinforcement learning:
 The agent gets positive reinforcement for tasks done well
 The agent gets negative reinforcement for tasks done poorly

3. Reinforcement Learning (cont.)
The goal is to get the agent to act in the
world so as to maximize its rewards
The agent has to figure out what it did that
made it get the reward/punishment
 This is known as the credit assignment problem
Reinforcement learning approaches can be
used to train computers to do many tasks
 backgammon and chess playing
 job shop scheduling
 controlling robot limbs

4. Reinforcement learning on the
web
Nifty applets:
 for blackjack
 for robot motion
 for a pendulum controller

5. Formalization
Given:
 a state space S
 a set of actions a1, …, ak
 reward value at the end of each trial (may
be positive or negative)
Output:
 a mapping from states to actions
example: Alvinn (driving agent)
state: configuration of the car
learn a steering action for each state

6. Accessible or
observable state
Repeat:
 s  sensed state
 If s is terminal then exit
 a  choose action (given s)
 Perform a
Reactive Agent Algorithm

7. Policy (Reactive/Closed-Loop Strategy)
• A policy P is a complete mapping from states to actions
-1
+1
2
3
1
4
3
2
1

8. Repeat:
 s  sensed state
 If s is terminal then exit
 a  P(s)
 Perform a
Reactive Agent Algorithm

9. Approaches
Learn policy directly– function mapping
from states to actions
Learn utility values for states (i.e., the
value function)

10. Value Function
The agent knows what state it is in
The agent has a number of actions it can perform in
each state.
Initially, it doesn't know the value of any of the states
If the outcome of performing an action at a state is
deterministic, then the agent can update the utility
value U() of states:
 U(oldstate) = reward + U(newstate)
The agent learns the utility values of states as it
works its way through the state space

11. Exploration
The agent may occasionally choose to explore
suboptimal moves in the hopes of finding better
outcomes
 Only by visiting all the states frequently enough can we
guarantee learning the true values of all the states
A discount factor is often introduced to prevent utility
values from diverging and to promote the use of
shorter (more efficient) sequences of actions to
attain rewards
The update equation using a discount factor  is:
 U(oldstate) = reward +  * U(newstate)
Normally,  is set between 0 and 1

12. Q-Learning
Q-learning augments value iteration by
maintaining an estimated utility value
Q(s,a) for every action at every state
The utility of a state U(s), or Q(s), is
simply the maximum Q value over all
the possible actions at that state
Learns utilities of actions (not states) 
model-free learning

13. Q-Learning
foreach state s
foreach action a
Q(s,a)=0
s=currentstate
do forever
a = select an action
do action a
r = reward from doing a
t = resulting state from doing a
Q(s,a) = (1 – ) Q(s,a) +  (r +  Q(t))
s = t
The learning coefficient, , determines how quickly our
estimates are updated
Normally,  is set to a small positive constant less than
1

14. Selecting an Action
Simply choose action with highest (current)
expected utility?
Problem: each action has two effects
 yields a reward (or penalty) on current sequence
 information is received and used in learning for
future sequences
Trade-off: immediate good for long-term well-
being
stuck in a rut
try a shortcut – you might get lost;
you might learn a new, quicker route!

15. Exploration policy
Wacky approach (exploration): act randomly
in hopes of eventually exploring entire
environment
Greedy approach (exploitation): act to
maximize utility using current estimate
Reasonable balance: act more wacky
(exploratory) when agent has little idea of
environment; more greedy when the model is
close to correct
Example: n-armed bandits…

16. RL Summary
Active area of research
Approaches from both OR and AI
There are many more sophisticated
algorithms that we have not discussed
Applicable to game-playing, robot
controllers, others