def test(self):
import rewardpredictive as rp
import numpy as np
rp.set_seeds(12345)
exp = rp.experiment.ExperimentCycleMDPDatasetPredictive({
rp.experiment.ExperimentCycleMDPDatasetPredictive.HP_REPEATS: 2
})
exp.run()
self.assertLessEqual(3., np.mean(exp.results['total_reward']))
count_list = [np.shape(c)[1] for c in exp.results['count']]
self.assertEqual(2., np.mean(count_list))
count_sum = [np.sum(c[-1]) for c in exp.results['count']]
self.assertTrue(np.all(np.array(count_sum) == 20))
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentCycleMDPDatasetPredictive({
rp.experiment.ExperimentCycleMDPDatasetPredictive.HP_REPEATS: 2
})
exp_reprod.run()
total_rew_exp = np.array(exp.results['total_reward'])
total_rew_exp_reprod = np.array(exp_reprod.results['total_reward'])
self.assertTrue(np.all(total_rew_exp == total_rew_exp_reprod))
count_exp = np.array(exp.results['count'])
count_exp_reprod = np.array(exp.results['count'])
self.assertTrue(np.all(count_exp == count_exp_reprod))
score_exp = np.array(exp.results['score'])
score_exp_reprod = np.array(exp.results['score'])
self.assertTrue(np.all(score_exp == score_exp_reprod))
def test_sftransfer(self):
import rewardpredictive as rp
rp.set_seeds(12345)
exp = rp.experiment.ExperimentGuitarSFTransfer({
rp.experiment.ExperimentGuitarSFTransfer.HP_REPEATS: 2
})
exp.run()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentGuitarSFTransfer({
rp.experiment.ExperimentGuitarSFTransfer.HP_REPEATS: 2
})
exp_reprod.run()
self._test_equality(exp, exp_reprod)
def test(self):
import rewardpredictive as rp
import numpy as np
rp.set_seeds(12345)
exp = rp.experiment.ExperimentRepresentationEvaluation()
exp.run()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentRepresentationEvaluation()
exp_reprod.run()
self.assertTrue(np.all(exp.results['partition_list'][0] == exp_reprod.results['partition_list'][0]))
self.assertTrue(np.all(exp.results['total_reward_list'][0] == exp_reprod.results['total_reward_list'][0]))
exp_rew_err = exp.results['reward_prediction_error_list'][0]
exp_reprod_rew_err = exp_reprod.results['reward_prediction_error_list'][0]
self.assertTrue(np.all(exp_rew_err == exp_reprod_rew_err))
def test_qlearning(self):
import rewardpredictive as rp
rp.set_seeds(12345)
exp = rp.experiment.ExperimentMazeQLearning({
rp.experiment.ExperimentMazeQLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazeQLearning.HP_REPEATS: 2
})
exp.run()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentMazeQLearning({
rp.experiment.ExperimentMazeQLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazeQLearning.HP_REPEATS: 2
})
exp_reprod.run()
self._test_episode_length(exp, exp_reprod)
def test_predictive(self):
import rewardpredictive as rp
import tensorflow as tf
tf.reset_default_graph()
rp.set_seeds(12345)
exp = rp.experiment.ExperimentGuitarRewardPredictive({
rp.experiment.ExperimentGuitarRewardPredictive.HP_REPEATS: 2
})
exp.run()
tf.reset_default_graph()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentGuitarRewardPredictive({
rp.experiment.ExperimentGuitarRewardPredictive.HP_REPEATS: 2
})
exp_reprod.run()
self._test_equality(exp, exp_reprod)
def test_maximizingq(self):
import rewardpredictive as rp
rp.set_seeds(12345)
exp = rp.experiment.ExperimentMazeMaximizingQLearning({
rp.experiment.ExperimentMazeMaximizingQLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazeMaximizingQLearning.HP_REPEATS: 2
})
exp.run()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentMazeMaximizingQLearning({
rp.experiment.ExperimentMazeMaximizingQLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazeMaximizingQLearning.HP_REPEATS: 2
})
exp_reprod.run()
self._test_episode_length(exp, exp_reprod)
self._test_mixture_agent_results(exp, exp_reprod)
def _test_exp_class(self, cls):
from itertools import product
import rewardpredictive as rp
for task_seq, exp in product(['slight', 'significant'], ['egreedy']):
hparam = {
cls.HP_NUM_EPISODES: 3,
cls.HP_REPEATS: 2,
cls.HP_TASK_SEQUENCE: task_seq,
cls.HP_EXPLORATION: exp
}
rp.set_seeds(12345)
exp = cls(hparam)
exp.run()
rp.set_seeds(12345)
exp_reprod = cls(hparam)
exp_reprod.run()
self._test_ep_len(exp, exp_reprod)
def test_predictivesf(self):
import rewardpredictive as rp
import tensorflow as tf
tf.reset_default_graph()
rp.set_seeds(12345)
exp = rp.experiment.ExperimentMazePredictiveSFLearning({
rp.experiment.ExperimentMazePredictiveSFLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazePredictiveSFLearning.HP_REPEATS: 2
})
exp.run()
tf.reset_default_graph()
rp.set_seeds(12345)
exp_reprod = rp.experiment.ExperimentMazePredictiveSFLearning({
rp.experiment.ExperimentMazePredictiveSFLearning.HP_NUM_EPISODES: 2,
rp.experiment.ExperimentMazePredictiveSFLearning.HP_REPEATS: 2
})
exp_reprod.run()
self._test_episode_length(exp, exp_reprod)
self._test_mixture_agent_results(exp, exp_reprod)