def testCreateRunner(self, mock_create_agent, mock_runner_constructor): base_dir = '/tmp' run_experiment.create_runner(base_dir) self.assertEqual(1, mock_runner_constructor.call_count) mock_args, _ = mock_runner_constructor.call_args self.assertEqual(base_dir, mock_args[0]) self.assertEqual(mock_create_agent, mock_args[1])
def testCreateTrainRunner(self, mock_create_agent, mock_runner_constructor): base_dir = '/tmp' run_experiment.create_runner(base_dir, schedule='continuous_train') self.assertEqual(1, mock_runner_constructor.call_count) mock_args, _ = mock_runner_constructor.call_args self.assertEqual(base_dir, mock_args[0]) self.assertEqual(mock_create_agent, mock_args[1])
def main(unused_argv): """Main method. Args: unused_argv: Arguments (unused). """ tf.logging.set_verbosity(tf.logging.INFO) run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings) runner = run_experiment.create_runner(FLAGS.base_dir) runner.run_experiment()
def main(unused_argv): """Main method. Args: unused_argv: Arguments (unused). """ logging.set_verbosity(logging.INFO) tf.compat.v1.disable_v2_behavior() run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings) runner = run_experiment.create_runner(FLAGS.base_dir) runner.run_experiment()
def main(unused_argv): """Main method. Args: unused_argv: Arguments (unused). """ tf.logging.set_verbosity(tf.logging.INFO) print("levin: test ok") run_experiment.load_gin_configs(FLAGS.gin_files, FLAGS.gin_bindings) FLAGS.base_dir = FLAGS.base_dir + '-' + str(int(time.time())) runner = run_experiment.create_runner(FLAGS.base_dir) runner.run_experiment()
RainbowAgent.epsilon_train = 0.01 RainbowAgent.epsilon_eval = 0.001 RainbowAgent.epsilon_decay_period = 250000 # agent steps # IQN currently does not support prioritized replay. RainbowAgent.replay_scheme = 'uniform' RainbowAgent.tf_device = '/gpu:0' # '/cpu:*' use for non-GPU version RainbowAgent.optimizer = @tf.train.AdamOptimizer() tf.train.AdamOptimizer.learning_rate = 0.00005 tf.train.AdamOptimizer.epsilon = 0.0003125 atari_lib.create_atari_environment.game_name = 'Pong' # Sticky actions with probability 0.25, as suggested by (Machado et al., 2017). atari_lib.create_atari_environment.sticky_actions = True create_agent.agent_name = 'implicit_quantile' Runner.num_iterations = 50 Runner.training_steps = 1000 Runner.evaluation_steps = 1000 Runner.max_steps_per_episode = 200 # Default max episode length. WrappedPrioritizedReplayBuffer.replay_capacity = 50000 WrappedPrioritizedReplayBuffer.batch_size = 128 """ gin.parse_config(iqn_config, skip_unknown=False) # @title Train IQN iqn_runner = run_experiment.create_runner(IQN_PATH, schedule='continuous_train') print('Will train IQN agent, please be patient, may be a while...') iqn_runner.run_experiment() print('Done training!')
def testCreateRunnerUnknown(self): base_dir = '/tmp' with self.assertRaisesRegexp(ValueError, 'Unknown schedule'): run_experiment.create_runner(base_dir, 'Unknown schedule')
#TrainRunner.create_environment_fn = @gym_lib.create_gym_environment Runner.num_iterations = 100 Runner.training_steps = 1000 Runner.evaluation_steps = 1000 Runner.max_steps_per_episode = 1000 # Default max episode length. WrappedReplayBuffer.replay_capacity = 100000 WrappedReplayBuffer.batch_size = 64 """ gin.parse_config(dqn_config, skip_unknown=False) for run in range(10): DQN_PATH = os.path.join(BASE_PATH, 'llv2_standard_eval_decay/run_' + str(run)) dqn_runner = run_experiment.create_runner( DQN_PATH, schedule='continuous_train_and_eval') print('Will train DQN agent, please be patient, may be a while...') dqn_runner.run_experiment() print('Done training!') data = colab_utils.read_experiment( DQN_PATH, verbose=True, summary_keys=[ 'train_episode_returns', 'train_episode_actual_returns', 'train_episode_lengths', 'eval_episode_returns', 'eval_episode_actual_returns', 'eval_episode_lengths' ]) data['agent'] = 'DQN' data['run'] = run
from __future__ import absolute_import from __future__ import division from __future__ import print_function from dopamine.discrete_domains import run_experiment import tensorflow as tf gin_files = ['./my_tests/fourier_pong.gin'] base_dir = './tmp/fourier/' gin_bindings = [] tf.logging.set_verbosity(tf.logging.INFO) run_experiment.load_gin_configs(gin_files, gin_bindings) runner = run_experiment.create_runner(base_dir) runner.run_experiment()