# Creating a New Representation¶

This tutorial describes the standard RLPy Representation interface, and illustrates a brief example of creating a new value function representation.

The Representation is the approximation of the value function associated with a Domain, usually in some lower-dimensional feature space.

The Agent receives observations from the Domain on each step and calls its learn() function, which is responsible for updating the Representation accordingly. Agents can later query the Representation for the value of being in a state V(s) or the value of taking an action in a particular state ( known as the Q-function, Q(s,a) ).

Note

At present, it is assumed that the Linear Function approximator family of representations is being used.

Note

You may want to review the namespace / inheritance / scoping rules in Python.

## Requirements¶

• Each Representation must be a subclass of Representation and call the __init__() function of the Representation superclass.
• Accordingly, each Representation must be instantiated with and a Domain in the __init__() function. Note that an optional discretization parameter may be used by discrete Representations attempting to represent a value function over a continuous space. It is ignored for discrete dimensions.
• 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, not random() or np.random(), as this will ensure consistent seeded results during experiments.
• After your Representation is complete, you should define a unit test to ensure future revisions do not alter behavior. See rlpy/tests/test_representations for some examples.

### REQUIRED Instance Variables¶

The new Representation MUST set the variables BEFORE calling the superclass __init__() function:

1. self.isDynamic - bool: True if this Representation can add or remove features during execution
2. self.features_num - int: The (initial) number of features in the representation

### REQUIRED Functions¶

The new Representation MUST define two functions:

1. phi_nonTerminal(), (see linked documentation), which returns a vector of feature function values associated with a particular state.
2. featureType(), (see linked documentation), which returns the data type of the underlying feature functions (eg “float” or “bool”).

### SPECIAL Functions¶

Representations whose feature functions may change over the course of execution (termed adaptive or dynamic Representations) should override one or both functions below as needed. Note that self.isDynamic should = True.

1. pre_discover()
2. post_discover()

• As always, the Representation can log messages using self.logger.info(<str>), see Python logger doc.
• You should log values assigned to custom parameters when __init__() is called.
• See Representation for functions provided by the superclass, especially before defining helper functions which might be redundant.

## Example: Creating the IncrementalTabular Representation¶

In this example we will recreate the simple IncrementalTabular Representation, which merely creates a binary feature function fd() that is associated with each discrete state d we have encountered so far. fd(s) = 1 when d=s, 0 elsewhere, ie, the vector of feature functions evaluated at s will have all zero elements except one. Note that this is identical to the Tabular Representation, except that feature functions are only created as needed, not instantiated for every single state at the outset. Though simple, neither the Tabular nor IncrementalTabular representations generalize to nearby states in the domain, and can be intractable to use on large domains (as there are as many feature functions as there are states in the entire space). Continuous dimensions of s (assumed to be bounded in this Representation) are discretized.

1. Create a new file in your current working directory, IncrTabularTut.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"]
__author__ = "Ray N. Forcement"

from rlpy.Representations.Representation import Representation
import numpy as np
from copy import deepcopy

2. Declare the class, create needed members variables (here an optional hash table to lookup feature function values previously computed), and write a docstring description:

class IncrTabularTut(Representation):
"""
Tutorial representation: identical to IncrementalTabular

"""
hash = None

3. Copy the __init__ declaration from Representation.py, add needed parameters (here none), and log them. Assign self.features_num and self.isDynamic, then call the superclass constructor:

def __init__(self, domain, discretization=20):
self.hash           = {}
self.features_num   = 0
self.isDynamic      = True
super(IncrTabularTut, self).__init__(domain, discretization)

4. Copy the phi_nonTerminal() function declaration and implement it accordingly to return the vector of feature function values for a given state. Here, lookup feature function values using self.hashState(s) provided by the parent class. Note here that self.hash should always contain hash_id if pre_discover() is called as required:

def phi_nonTerminal(self, s):
hash_id = self.hashState(s)
id  = self.hash.get(hash_id)
F_s = np.zeros(self.features_num, bool)
if id is not None:
F_s[id] = 1
return F_s

5. Copy the featureType() function declaration and implement it accordingly to return the datatype returned by each feature function. Here, feature functions are binary, so the datatype is boolean:

def featureType(self):
return bool

6. Override parent functions as necessary; here we require a pre_discover() function to populate the hash table for each new encountered state:

def pre_discover(self, s, terminal, a, sn, terminaln):

7. Finally, define any needed helper functions:

def _add_state(self, s):
hash_id = self.hashState(s)
id  = self.hash.get(hash_id)
if id is None:
#New State
self.features_num += 1
#New id = feature_num - 1
id = self.features_num - 1
self.hash[hash_id] = id
#Add a new element to the feature weight vector
return 1
return 0

def __deepcopy__(self, memo):
new_copy = IncrementalTabular(self.domain, self.discretization)
new_copy.hash = deepcopy(self.hash)
return new_copy


That’s it! Now test your Representation 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 #!/usr/bin/env python """ Representation Tutorial for RLPy ================================ Assumes you have created the IncrTabularTut.py agent according to the tutorial and placed it in the current working directory. Tests the Representation on the GridWorld domain usin SARSA """ __author__ = "Robert H. Klein" from rlpy.Domains import GridWorld from rlpy.Agents import SARSA from IncrTabularTut import IncrTabularTut from rlpy.Policies import eGreedy from rlpy.Experiments import Experiment import os def make_experiment(exp_id=1, path="./Results/Tutorial/gridworld-IncrTabularTut"): """ 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 = IncrTabularTut(domain) ## Policy policy = eGreedy(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 Representation tutorial, we have seen how to

• Write an adaptive Representation that inherits from the RLPy base Representation class
• Add the Representation to RLPy and test it

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"]
__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 representation can be added to the __init__.py file in the Representations/ directory. (This allows other files to import the new representation).
• 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 representation 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.