def run_exp(env,
policy,
hp,
steps,
dir_name,
evaluate,
seed,
eval_interval,
log_interval,
save_interval,
initial_exploration_steps):
"""Run a single training procedure.
Parameters
----------
env : str or gym.Env
the training/testing environment
policy : type [ hbaselines.base_policies.Policy ]
the policy class to use
hp : dict
additional algorithm hyper-parameters
steps : int
total number of training steps
dir_name : str
the location the results files are meant to be stored
evaluate : bool
whether to include an evaluation environment
seed : int
specified the random seed for numpy, tensorflow, and random
eval_interval : int
number of simulation steps in the training environment before an
evaluation is performed
log_interval : int
the number of training steps before logging training results
save_interval : int
number of simulation steps in the training environment before the model
is saved
initial_exploration_steps : int
number of timesteps that the policy is run before training to
initialize the replay buffer with samples
"""
eval_env = env if evaluate else None
alg = RLAlgorithm(
policy=policy,
env=env,
eval_env=eval_env,
**hp
)
# perform training
alg.learn(
total_steps=steps,
log_dir=dir_name,
log_interval=log_interval,
eval_interval=eval_interval,
save_interval=save_interval,
initial_exploration_steps=initial_exploration_steps,
seed=seed,
)
def test_learn_initial_exploration_steps(self):
"""Test the initial_exploration_steps parameter in the learn method.
This is done for the following cases:
1. initial_exploration_steps= = 0
2. initial_exploration_steps= = 100
"""
# =================================================================== #
# test case 1 #
# =================================================================== #
# Create the algorithm object.
policy_params = self.init_parameters.copy()
policy_params['policy'] = FeedForwardPolicy
policy_params['_init_setup_model'] = True
alg = RLAlgorithm(**policy_params)
# Run the learn operation for zero exploration steps.
alg.learn(0, log_dir='results', initial_exploration_steps=0)
# Check the size of the replay buffer
self.assertEqual(len(alg.policy_tf.replay_buffer), 0)
# Clear memory.
del alg
shutil.rmtree('results')
# =================================================================== #
# test case 2 #
# =================================================================== #
# Create the algorithm object.
policy_params = self.init_parameters.copy()
policy_params['policy'] = FeedForwardPolicy
policy_params['_init_setup_model'] = True
alg = RLAlgorithm(**policy_params)
# Run the learn operation for zero exploration steps.
alg.learn(0, log_dir='results', initial_exploration_steps=100)
# Check the size of the replay buffer
self.assertEqual(len(alg.policy_tf.replay_buffer), 100)
# Clear memory.
del alg
shutil.rmtree('results')
def test_learn_init(self):
"""Test the non-loop components of the `learn` method."""
# Create the algorithm object.
policy_params = self.init_parameters.copy()
policy_params['policy'] = GoalConditionedPolicy
policy_params['_init_setup_model'] = True
alg = RLAlgorithm(**policy_params)
# Run the learn operation for zero steps.
alg.learn(0, log_dir='results', initial_exploration_steps=0)
self.assertEqual(alg.episodes, 0)
self.assertEqual(alg.total_steps, 0)
self.assertEqual(alg.epoch, 0)
self.assertEqual(len(alg.episode_rew_history), 0)
self.assertEqual(alg.epoch_episodes, 0)
self.assertEqual(len(alg.epoch_episode_rewards), 0)
self.assertEqual(len(alg.epoch_episode_steps), 0)
shutil.rmtree('results')
# Test the seeds.
alg.learn(0, log_dir='results', seed=1, initial_exploration_steps=0)
self.assertEqual(np.random.sample(), 0.417022004702574)
self.assertEqual(random.uniform(0, 1), 0.13436424411240122)
shutil.rmtree('results')
