193 lines
6.0 KiB
Python
Executable File
193 lines
6.0 KiB
Python
Executable File
"""
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experiment.py
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Curiosity project Experiment class definition.
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Aim for better encapsulation.
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Experiment class
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- This class should get the various classes to use in running an experiment
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- EvolveWeights
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- mda?
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- Environ (GridWorld, ConvBelt, Puzzle)
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- Still is going to require ad hoc function to create the particular Environ
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- But could pass in function to use
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- Agentclass
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- And experimental attributes
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- For example
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- Experiment constructs EW instance, passes in weight length
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- Experiment constructs Environ instance
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- Experiment requests evolution run of EW with parameters
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- EW calls Experiment for each evaluation of an individual (and in what generation)
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- Experiment calls Environ.evaluate with individual weights, agentclass
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- Passes w, tuple back to EW
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"""
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import sys
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import os
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import traceback
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class Holder(object):
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def __init__(self):
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pass
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class Experiment(object):
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"""
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Experiment class. Instances will drive reinforcement learning experiments.
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"""
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def __init__(self):
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self.agentclass = None
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self.environclass = None
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self.evolverclass = None
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self.environmaker = None
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pass
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def validate(self):
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valid = True
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# Test that we have classes to use
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valid = valid and (not self.agentclass in [None])
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valid = valid and (not self.environclass in [None])
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valid = valid and (not self.evolverclass in [None])
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# Test other values here
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return valid
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def set_schedule(self, schedule):
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self.schedule = schedule
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def set_environ_maker(self, environmaker):
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self.environmaker = environmaker
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def make_environ(self):
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if not self.environmaker in [None]:
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try:
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self.environ = self.environmaker()
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except:
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estr = f"Error: traceback.format_exc()"
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print(estr)
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self.environ = None
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assert 0, "Creating environment failed. Quitting."
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def set_agentclass(self, agentclass):
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# Test class for compatibility
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okclass = True
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# No test yet
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if okclass:
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self.agentclass = agentclass
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def get_agentclass(self):
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return self.agentclass
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def set_environclass(self, environclass):
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# Test class for compatibility
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okclass = True
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if not 'evaluate' in dir(environclass):
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okclass = False
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print("set_environclass error: class does not provide 'evaluate'")
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if okclass:
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self.environclass = environclass
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def get_environclass(self):
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return self.environclass
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def set_evolverclass(self, evolverclass):
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# Test class for compatibility
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okclass = True
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if not 'driver' in dir(evolverclass):
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okclass = False
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print("set_evolverclass error: class does not provide 'driver'")
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if okclass:
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self.evolverclass = evolverclass
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def set_agent_attributes(self, alpha=0.005):
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self.agent_props = Holder()
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self.agent_props.alpha = 0.005
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def set_evolver_attributes(self,
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popsize=100,
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maxgenerations=10000,
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cxpb=0.5,
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mtpb=0.05,
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wmin=-20.0,
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wmax=20.0,
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mut_center=0.0,
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mut_sigma=0.1,
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mut_indpb=0.05,
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tournsize=5,
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tournk=2,
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normalize_fitness=True,
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tag='environ'
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):
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self.evolver_props = Holder()
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self.evolver_props.popsize = popsize
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self.evolver_props.maxgenerations = maxgenerations
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self.evolver_props.cxpb = cxpb
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self.evolver_props.mtpb = mtpb
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self.evolver_props.wmin = wmin
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self.evolver_props.wmax = wmax
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self.evolver_props.mut_center = mut_center
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self.evolver_props.mut_sigma = mut_sigma
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self.evolver_props.mut_indpb = mut_indpb
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self.evolver_props.tournsize = tournsize
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self.evolver_props.tournk = tournk
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self.evolver_props.normalize_fitness = normalize_fitness
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self.evolver_props.tag = tag
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def make_evolver_instance(self):
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self.evolver = self.evolverclass(
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# self.environclass,
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# weights_len
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weights_len=self.environ.get_weights_len(),
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# alpha
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alpha=self.environ.alpha,
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# evaluate function
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evaluate=self.environ.evaluate,
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popsize=self.evolver_props.popsize,
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maxgenerations=self.evolver_props.maxgenerations,
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cxpb=self.evolver_props.cxpb,
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mtpb=self.evolver_props.mtpb,
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wmin=self.evolver_props.wmin,
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wmax=self.evolver_props.wmax,
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mut_center= self.evolver_props.mut_center,
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mut_sigma= self.evolver_props.mut_sigma,
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mut_indpb= self.evolver_props.mut_indpb,
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tournsize= self.evolver_props.tournsize,
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tournk= self.evolver_props.tournk,
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normalize_fitness= self.evolver_props.normalize_fitness,
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tag= self.evolver_props.tag
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)
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def set_env_attributes(self):
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self.env_props = Holder()
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def handle_evaluation(self, ind, generation):
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"""
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evolver calls this to get an evaluation of an
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individual.
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Depending on the experiment schedule and generation,
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this may require constructing a new environment.
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"""
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pass
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def run_experiment(self):
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"""
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# Run experiment
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ew = EvolveWeights(world,
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popsize=100,
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maxgenerations=1000,
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tournsize=75,
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tournk=3,
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normalize_fitness=False)
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ew.driver()
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"""
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