Creating a New Policy¶
This tutorial describes the standard
RLPy Policy
interface,
and illustrates a brief example of creating a new problem domain.
The Policy determines the discrete action that an
Agent
will take given its current value function
Representation
.
The Agent learns about the Domain
as the two interact.
At each step, the Agent passes information about its current state
to the Policy; the Policy uses this to decide what discrete action the
Agent should perform next (see pi()
) n
Warning
While each dimension of the state s is either continuous or discrete, discrete dimensions are assume to take nonnegative integer values (ie, the index of the discrete state).
Note
You may want to review the namespace / inheritance / scoping rules in Python.
Requirements¶
- Each Policy must be a subclass of
Policy
and call the__init__()
function of the Policy superclass. - Any randomization that occurs at object construction MUST occur in
the
init_randomization()
function, which can be called by__init__()
. - Any random calls should use
self.random_state
, notrandom()
ornp.random()
, as this will ensure consistent seeded results during experiments. - After your Policy is complete, you should define a unit test to ensure future revisions do not alter behavior. See rlpy/tests for some examples.
REQUIRED Instance Variables¶
—
REQUIRED Functions¶
pi()
- accepts the current state s, whether or not s is terminal, and an array of possible actions indices p_actions and returns an action index for the Agent to take.
SPECIAL Functions¶
Policies which have an explicit exploratory component (eg epsilon-greedy) MUST override the functions below to prevent exploratory behavior when evaluating the policy (which would skew results)
turnOffExploration()
turnOnExploration()
Additional Information¶
- As always, the Policy can log messages using
self.logger.info(<str>)
, see Pythonlogger
documentation. - You should log values assigned to custom parameters when
__init__()
is called. - See
Policy
for functions provided by the superclass.
Example: Creating the Epsilon-Greedy
Policy¶
In this example we will recreate the eGreedy
Policy.
From a given state, it selects the action with the highest expected value
(greedy with respect to value function), but with some probability epsilon
,
takes a random action instead. This explicitly balances the exploration/exploitation
tradeoff, and ensures that in the limit of infinite samples, the agent will
have explored the entire domain.
Create a new file in your current working directory,
eGreedyTut.py
. Add the header block at the top:__copyright__ = "Copyright 2013, RLPy http://www.acl.mit.edu/RLPy" __credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann", "William Dabney", "Jonathan P. How"] __license__ = "BSD 3-Clause" __author__ = "Ray N. Forcement" from rlpy.Policies.Policy import Policy import numpy as np
Declare the class, create needed members variables, and write a docstring description. See the role of member variables in comments:
class eGreedyTut(Policy): """ From the tutorial in policy creation. Identical to eGreedy.py. """ # Probability of selecting a random action instead of greedy epsilon = None # Temporarily stores value of ``epsilon`` when exploration disabled old_epsilon = None # bool, used to avoid random selection among actions with the same values forcedDeterministicAmongBestActions = None
Copy the
__init__()
declaration fromPolicy.py
and add needed parameters. In the function body, assign them and log them. Then call the superclass constructor. Here the parameters are the probability of selecting a random action,epsilon
, and how to handle the case where multiple best actions exist, ie with the same value,forcedDeterministicAmongBestActions
:def __init__(self,representation, epsilon = .1, forcedDeterministicAmongBestActions = False, seed=1): self.epsilon = epsilon self.forcedDeterministicAmongBestActions = forcedDeterministicAmongBestActions super(eGreedyTut,self).__init__(representation)
Copy the
pi()
declaration fromPolicy.py
and implement it to return an action index for any given state and possible action inputs. Here, with probability epsilon, take a random action among the possible. Otherwise, pick an action with the highest expected value (depending onself.forcedDeterministicAmongBestActions
, either pick randomly from among the best actions or always select the one with lowest index:def pi(self,s, terminal, p_actions): coin = self.random_state.rand() #print "coin=",coin if coin < self.epsilon: return self.random_state.choice(p_actions) else: b_actions = self.representation.bestActions(s, terminal, p_actions) if self.forcedDeterministicAmongBestActions: return b_actions[0] else: return self.random_state.choice(b_actions)
Because this policy has an exploratory component, we must override the
turnOffExploration()
andturnOnExploration()
functions, so that when evaluating the policy’s performance the exploratory component may be automatically disabled so as not to influence results:def turnOffExploration(self): self.old_epsilon = self.epsilon self.epsilon = 0 def turnOnExploration(self): self.epsilon = self.old_epsilon
Warning
If you fail to define turnOffExploration()
and turnOnExploration()
for functions with exploratory components, measured algorithm performance
will be worse, since exploratory actions by definition are suboptimal based
on the current model.
That’s it! Now test your new Policy by creating a simple settings file on the domain of your choice. An example experiment is given below:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 | #!/usr/bin/env python
"""
Policy Tutorial for RLPy
=================================
Assumes you have created the eGreedyTut.py agent according to the tutorial and
placed it in the current working directory.
Tests the policy on the GridWorld domain, with the policy and value function
visualized.
"""
__author__ = "Robert H. Klein"
from rlpy.Domains import GridWorld
from rlpy.Agents import SARSA
from rlpy.Representations import Tabular
from eGreedyTut import eGreedyTut
from rlpy.Experiments import Experiment
import os
def make_experiment(exp_id=1, path="./Results/Tutorial/gridworld-eGreedyTut"):
"""
Each file specifying an experimental setup should contain a
make_experiment function which returns an instance of the Experiment
class with everything set up.
@param id: number used to seed the random number generators
@param path: output directory where logs and results are stored
"""
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
## Domain:
maze = '4x5.txt'
domain = GridWorld(maze, noise=0.3)
opt["domain"] = domain
## Representation
# discretization only needed for continuous state spaces, discarded otherwise
representation = Tabular(domain, discretization=20)
## Policy
policy = eGreedyTut(representation, epsilon=0.2)
## Agent
opt["agent"] = SARSA(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1)
opt["checks_per_policy"] = 100
opt["max_steps"] = 2000
opt["num_policy_checks"] = 10
experiment = Experiment(**opt)
return experiment
if __name__ == '__main__':
experiment = make_experiment(1)
experiment.run(visualize_steps=False, # should each learning step be shown?
visualize_learning=True, # show policy / value function?
visualize_performance=1) # show performance runs?
experiment.plot()
experiment.save()
|
What to do next?¶
In this Policy tutorial, we have seen how to
- Write a Policy that inherits from the RLPy base
Policy
class - Override several base functions, including those that manage exploration/exploitation
- Add the Policy to RLPy and test it
Adding your component to RLPy¶
If you would like to add your component to RLPy, we recommend developing on the development version (see Development Version). Please use the following header at the top of each file:
__copyright__ = "Copyright 2013, RLPy http://www.acl.mit.edu/RLPy"
__credits__ = ["Alborz Geramifard", "Robert H. Klein", "Christoph Dann",
"William Dabney", "Jonathan P. How"]
__license__ = "BSD 3-Clause"
__author__ = "Tim Beaver"
- Fill in the appropriate
__author__
name and__credits__
as needed. Note that RLPy requires the BSD 3-Clause license. - If you installed RLPy in a writeable directory, the className of the new
policy can be added to
the
__init__.py
file in thePolicies/
directory. (This allows other files to import the new policy). - If available, please include a link or reference to the publication associated with this implementation (and note differences, if any).
If you would like to add your new policy to the RLPy project, we recommend you branch the project and create a pull request to the RLPy repository.
You can also email the community list rlpy@mit.edu
for comments or
questions. To subscribe click here.