<s> from rlpy . Domains import PST <EOL> from rlpy . Agents import Greedy_GQ <EOL> from rlpy . Representations import * <EOL> from rlpy . Policies import eGreedy <EOL> from rlpy . Experiments import Experiment <EOL> import numpy as np <EOL> from hyperopt import hp <EOL> param_space = { <EOL> '<STR_LIT>' : hp . loguniform ( "<STR_LIT>" , <EOL> np . log ( <NUM_LIT> ) , np . log ( <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : hp . loguniform ( "<STR_LIT>" , np . log ( <NUM_LIT> ) , np . log ( <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : hp . loguniform ( "<STR_LIT>" , np . log ( <NUM_LIT> ) , np . log ( <NUM_LIT:1> ) ) } <EOL> def make_experiment ( <EOL> exp_id = <NUM_LIT:1> , path = "<STR_LIT>" , <EOL> discover_threshold = <NUM_LIT> , <EOL> lambda_ = <NUM_LIT:0.> , <EOL> boyan_N0 = <NUM_LIT> , <EOL> initial_learn_rate = <NUM_LIT> ) : <EOL> opt = { } <EOL> opt [ "<STR_LIT:path>" ] = path <EOL> opt [ "<STR_LIT>" ] = exp_id <EOL> opt [ "<STR_LIT>" ] = <NUM_LIT> <EOL> opt [ "<STR_LIT>" ] = <NUM_LIT:30> <EOL> opt [ "<STR_LIT>" ] = <NUM_LIT:10> <EOL> sparsify = <NUM_LIT:1> <EOL> ifddeps = <NUM_LIT> <EOL> beta_coef = <NUM_LIT> <EOL> domain = PST ( NUM_UAV = <NUM_LIT:4> ) <EOL> opt [ "<STR_LIT>" ] = domain <EOL> initial_rep = IndependentDiscretization ( domain ) <EOL> representation = iFDD ( domain , discover_threshold , initial_rep , <EOL> sparsify = sparsify , <EOL> useCache = True , <EOL> iFDDPlus = <NUM_LIT:1> - ifddeps ) <EOL> policy = eGreedy ( representation , epsilon = <NUM_LIT:0.1> ) <EOL> opt [ "<STR_LIT>" ] = Greedy_GQ ( policy , representation , <EOL> discount_factor = domain . discount_factor , <EOL> BetaCoef = beta_coef , <EOL> lambda_ = lambda_ , initial_learn_rate = initial_learn_rate , <EOL> learn_rate_decay_mode = "<STR_LIT>" , boyan_N0 = boyan_N0 ) <EOL> experiment = Experiment ( ** opt ) <EOL> return experiment <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> from rlpy . Tools . run import run_profiled <EOL> run_profiled ( make_experiment ) </s>
