def run_task(vv, log_dir=None, exp_name=None): # Load environment radius = vv['radius'] target_velocity = vv['target_velocity'] env = normalize(CircleEnv(radius, target_velocity)) # Save variant information for comparison plots variant_file = logger.get_snapshot_dir() + '/variant.json' logger.log_variant(variant_file, vv) # Train policy using TRPO policy = GaussianMLPPolicy( env_spec=env.spec, hidden_sizes=(32, 32) ) baseline = LinearFeatureBaseline(env_spec=env.spec) algo = TRPO( env=env, policy=policy, baseline=baseline, batch_size=1000, max_path_length=env.horizon, n_itr=1000, discount=0.99, step_size=0.01, plot=False, ) algo.train()
def setup_logger( exp_prefix=None, exp_count=0, seed=0, variant=None, log_dir=None, text_log_file="debug.log", variant_log_file="variant.json", tabular_log_file="progress.csv", snapshot_mode="last", log_tabular_only=False, snapshot_gap=1, ): """ Set up logger to have some reasonable default settings. :param exp_prefix: :param exp_count: :param seed: Experiment seed :param variant: :param log_dir: :param text_log_file: :param variant_log_file: :param tabular_log_file: :param snapshot_mode: :param log_tabular_only: :param snapshot_gap: :return: """ if log_dir is None: assert exp_prefix is not None log_dir = create_log_dir(exp_prefix, exp_count=exp_count, seed=seed) tabular_log_path = osp.join(log_dir, tabular_log_file) text_log_path = osp.join(log_dir, text_log_file) if variant is not None: variant_log_path = osp.join(log_dir, variant_log_file) logger.log_variant(variant_log_path, variant) logger.add_text_output(text_log_path) logger.add_tabular_output(tabular_log_path) logger.set_snapshot_dir(log_dir) logger.set_snapshot_mode(snapshot_mode) logger.set_snapshot_gap(snapshot_gap) logger.set_log_tabular_only(log_tabular_only)
def run_experiment(argv): # e2crawfo: These imports, in this order, were necessary for fixing issues on cedar. import rllab.mujoco_py.mjlib import tensorflow default_log_dir = config.LOG_DIR now = datetime.datetime.now(dateutil.tz.tzlocal()) # avoid name clashes when running distributed jobs rand_id = str(uuid.uuid4())[:5] timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z') default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id) parser = argparse.ArgumentParser() parser.add_argument( '--n_parallel', type=int, default=1, help= 'Number of parallel workers to perform rollouts. 0 => don\'t start any workers' ) parser.add_argument('--exp_name', type=str, default=default_exp_name, help='Name of the experiment.') parser.add_argument('--log_dir', type=str, default=None, help='Path to save the log and iteration snapshot.') parser.add_argument('--snapshot_mode', type=str, default='all', help='Mode to save the snapshot. Can be either "all" ' '(all iterations will be saved), "last" (only ' 'the last iteration will be saved), or "none" ' '(do not save snapshots)') parser.add_argument('--snapshot_gap', type=int, default=1, help='Gap between snapshot iterations.') parser.add_argument('--tabular_log_file', type=str, default='progress.csv', help='Name of the tabular log file (in csv).') parser.add_argument('--text_log_file', type=str, default='debug.log', help='Name of the text log file (in pure text).') parser.add_argument('--params_log_file', type=str, default='params.json', help='Name of the parameter log file (in json).') parser.add_argument('--variant_log_file', type=str, default='variant.json', help='Name of the variant log file (in json).') parser.add_argument( '--resume_from', type=str, default=None, help='Name of the pickle file to resume experiment from.') parser.add_argument('--plot', type=ast.literal_eval, default=False, help='Whether to plot the iteration results') parser.add_argument( '--log_tabular_only', type=ast.literal_eval, default=False, help= 'Whether to only print the tabular log information (in a horizontal format)' ) parser.add_argument('--seed', type=int, help='Random seed for numpy') parser.add_argument('--args_data', type=str, help='Pickled data for stub objects') parser.add_argument('--variant_data', type=str, help='Pickled data for variant configuration') parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False) args = parser.parse_args(argv[1:]) if args.seed is not None: set_seed(args.seed) if args.n_parallel > 0: from rllab.sampler import parallel_sampler parallel_sampler.initialize(n_parallel=args.n_parallel) if args.seed is not None: parallel_sampler.set_seed(args.seed) if args.plot: from rllab.plotter import plotter plotter.init_worker() if args.log_dir is None: log_dir = osp.join(default_log_dir, args.exp_name) else: log_dir = args.log_dir tabular_log_file = osp.join(log_dir, args.tabular_log_file) text_log_file = osp.join(log_dir, args.text_log_file) params_log_file = osp.join(log_dir, args.params_log_file) if args.variant_data is not None: variant_data = pickle.loads(base64.b64decode(args.variant_data)) variant_log_file = osp.join(log_dir, args.variant_log_file) logger.log_variant(variant_log_file, variant_data) else: variant_data = None if not args.use_cloudpickle: logger.log_parameters_lite(params_log_file, args) logger.add_text_output(text_log_file) logger.add_tabular_output(tabular_log_file) prev_snapshot_dir = logger.get_snapshot_dir() prev_mode = logger.get_snapshot_mode() logger.set_snapshot_dir(log_dir) logger.set_tf_summary_dir(osp.join(log_dir, "tf_summary")) logger.set_snapshot_mode(args.snapshot_mode) logger.set_snapshot_gap(args.snapshot_gap) logger.set_log_tabular_only(args.log_tabular_only) logger.push_prefix("[%s] " % args.exp_name) if args.resume_from is not None: data = joblib.load(args.resume_from) assert 'algo' in data algo = data['algo'] maybe_iter = algo.train() if is_iterable(maybe_iter): for _ in maybe_iter: pass else: # read from stdin if args.use_cloudpickle: import cloudpickle method_call = cloudpickle.loads(base64.b64decode(args.args_data)) method_call(variant_data) else: data = pickle.loads(base64.b64decode(args.args_data)) maybe_iter = concretize(data) if is_iterable(maybe_iter): for _ in maybe_iter: pass logger.set_snapshot_mode(prev_mode) logger.set_snapshot_dir(prev_snapshot_dir) logger.remove_tabular_output(tabular_log_file) logger.remove_text_output(text_log_file) logger.pop_prefix()
def run_experiment(argv): default_log_dir = config.LOG_DIR now = datetime.datetime.now(dateutil.tz.tzlocal()) # avoid name clashes when running distributed jobs rand_id = str(uuid.uuid4())[:5] timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z') default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id) parser = argparse.ArgumentParser() parser.add_argument('--n_parallel', type=int, default=1, help='Number of parallel workers to perform rollouts. 0 => don\'t start any workers') parser.add_argument( '--exp_name', type=str, default=default_exp_name, help='Name of the experiment.') parser.add_argument('--log_dir', type=str, default=None, help='Path to save the log and iteration snapshot.') parser.add_argument('--snapshot_mode', type=str, default='all', help='Mode to save the snapshot. Can be either "all" ' '(all iterations will be saved), "last" (only ' 'the last iteration will be saved), "gap" (every' '`snapshot_gap` iterations are saved), or "none" ' '(do not save snapshots)') parser.add_argument('--snapshot_gap', type=int, default=1, help='Gap between snapshot iterations.') parser.add_argument('--tabular_log_file', type=str, default='progress.csv', help='Name of the tabular log file (in csv).') parser.add_argument('--text_log_file', type=str, default='debug.log', help='Name of the text log file (in pure text).') parser.add_argument('--params_log_file', type=str, default='params.json', help='Name of the parameter log file (in json).') parser.add_argument('--variant_log_file', type=str, default='variant.json', help='Name of the variant log file (in json).') parser.add_argument('--resume_from', type=str, default=None, help='Name of the pickle file to resume experiment from.') parser.add_argument('--plot', type=ast.literal_eval, default=False, help='Whether to plot the iteration results') parser.add_argument('--log_tabular_only', type=ast.literal_eval, default=False, help='Whether to only print the tabular log information (in a horizontal format)') parser.add_argument('--seed', type=int, help='Random seed for numpy') parser.add_argument('--args_data', type=str, help='Pickled data for stub objects') parser.add_argument('--variant_data', type=str, help='Pickled data for variant configuration') parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False) args = parser.parse_args(argv[1:]) if args.seed is not None: set_seed(args.seed) if args.n_parallel > 0: from rllab.sampler import parallel_sampler parallel_sampler.initialize(n_parallel=args.n_parallel) if args.seed is not None: parallel_sampler.set_seed(args.seed) if args.plot: from rllab.plotter import plotter plotter.init_worker() if args.log_dir is None: log_dir = osp.join(default_log_dir, args.exp_name) else: log_dir = args.log_dir tabular_log_file = osp.join(log_dir, args.tabular_log_file) text_log_file = osp.join(log_dir, args.text_log_file) params_log_file = osp.join(log_dir, args.params_log_file) if args.variant_data is not None: variant_data = pickle.loads(base64.b64decode(args.variant_data)) variant_log_file = osp.join(log_dir, args.variant_log_file) logger.log_variant(variant_log_file, variant_data) else: variant_data = None if not args.use_cloudpickle: logger.log_parameters_lite(params_log_file, args) logger.add_text_output(text_log_file) logger.add_tabular_output(tabular_log_file) prev_snapshot_dir = logger.get_snapshot_dir() prev_mode = logger.get_snapshot_mode() logger.set_snapshot_dir(log_dir) logger.set_snapshot_mode(args.snapshot_mode) logger.set_snapshot_gap(args.snapshot_gap) logger.set_log_tabular_only(args.log_tabular_only) logger.push_prefix("[%s] " % args.exp_name) if args.resume_from is not None: data = joblib.load(args.resume_from) assert 'algo' in data algo = data['algo'] algo.train() else: # read from stdin if args.use_cloudpickle: import cloudpickle method_call = cloudpickle.loads(base64.b64decode(args.args_data)) method_call(variant_data) else: data = pickle.loads(base64.b64decode(args.args_data)) maybe_iter = concretize(data) if is_iterable(maybe_iter): for _ in maybe_iter: pass logger.set_snapshot_mode(prev_mode) logger.set_snapshot_dir(prev_snapshot_dir) logger.remove_tabular_output(tabular_log_file) logger.remove_text_output(text_log_file) logger.pop_prefix()
def run_task(vv, log_dir=None, exp_name=None): global policy global baseline trpo_stepsize = 0.01 trpo_subsample_factor = 0.2 # Check if variant is available if vv['model_type'] not in ['BrushTireModel', 'LinearTireModel']: raise ValueError('Unrecognized model type for simulating robot') if vv['robot_type'] not in ['MRZR', 'RCCar']: raise ValueError('Unrecognized robot type') # Load environment if not vv['use_ros']: env = CircleEnv(target_velocity=vv['target_velocity'], radius=vv['radius'], dt=vv['dt'], model_type=vv['model_type'], robot_type=vv['robot_type']) else: from aa_simulation.envs.circle.circle_env_ros import CircleEnvROS env = CircleEnvROS(target_velocity=vv['target_velocity'], radius=vv['radius'], dt=vv['dt'], model_type=vv['model_type'], robot_type=vv['robot_type']) # Save variant information for comparison plots variant_file = logger.get_snapshot_dir() + '/variant.json' logger.log_variant(variant_file, vv) # Set variance for each action component separately for exploration # Note: We set the variance manually because we are not scaling our # action space during training. init_std_speed = vv['target_velocity'] / 4 init_std_steer = np.pi / 6 init_std = [init_std_speed, init_std_steer] # Build policy and baseline networks # Note: Mean of policy network set to analytically computed values for # faster training (rough estimates for RL to fine-tune). if policy is None or baseline is None: wheelbase = 0.257 target_velocity = vv['target_velocity'] target_steering = np.arctan(wheelbase / vv['radius']) # CCW output_mean = np.array([target_velocity, target_steering]) hidden_sizes = (32, 32) # In mean network, allow output b values to dominate final output # value by constraining the magnitude of the output W matrix. This is # to allow faster learning. These numbers are arbitrarily chosen. W_gain = min(vv['target_velocity'] / 5, np.pi / 15) mean_network = MLP(input_shape=(env.spec.observation_space.flat_dim, ), output_dim=env.spec.action_space.flat_dim, hidden_sizes=hidden_sizes, hidden_nonlinearity=LN.tanh, output_nonlinearity=None, output_W_init=LI.GlorotUniform(gain=W_gain), output_b_init=output_mean) policy = GaussianMLPPolicy(env_spec=env.spec, hidden_sizes=hidden_sizes, init_std=init_std, mean_network=mean_network) baseline = LinearFeatureBaseline(env_spec=env.spec, target_key='returns') # Reset variance to re-enable exploration when using pre-trained networks else: policy._l_log_std = ParamLayer( policy._mean_network.input_layer, num_units=env.spec.action_space.flat_dim, param=LI.Constant(np.log(init_std)), name='output_log_std', trainable=True) obs_var = policy._mean_network.input_layer.input_var mean_var, log_std_var = L.get_output( [policy._l_mean, policy._l_log_std]) policy._log_std_var = log_std_var LasagnePowered.__init__(policy, [policy._l_mean, policy._l_log_std]) policy._f_dist = ext.compile_function(inputs=[obs_var], outputs=[mean_var, log_std_var]) safety_baseline = LinearFeatureBaseline(env_spec=env.spec, target_key='safety_returns') safety_constraint = CircleSafetyConstraint(max_value=1.0, eps=vv['eps'], baseline=safety_baseline) if vv['algo'] == 'TRPO': algo = TRPO( env=env, policy=policy, baseline=baseline, batch_size=600, max_path_length=env.horizon, n_itr=600, discount=0.99, step_size=trpo_stepsize, plot=False, ) else: algo = CPO(env=env, policy=policy, baseline=baseline, safety_constraint=safety_constraint, batch_size=600, max_path_length=env.horizon, n_itr=600, discount=0.99, step_size=trpo_stepsize, gae_lambda=0.95, safety_gae_lambda=1, optimizer_args={'subsample_factor': trpo_subsample_factor}, plot=False) algo.train()
def run_experiment(argv): default_log_dir = config.LOG_DIR now = datetime.datetime.now(dateutil.tz.tzlocal()) # avoid name clashes when running distributed jobs rand_id = str(uuid.uuid4())[:5] timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z') default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id) parser = argparse.ArgumentParser() parser.add_argument( '--n_parallel', type=int, default=1, help= 'Number of parallel workers to perform rollouts. 0 => don\'t start any workers' ) parser.add_argument('--exp_name', type=str, default=default_exp_name, help='Name of the experiment.') parser.add_argument('--log_dir', type=str, default=None, help='Path to save the log and iteration snapshot.') parser.add_argument('--snapshot_mode', type=str, default='all', help='Mode to save the snapshot. Can be either "all" ' '(all iterations will be saved), "last" (only ' 'the last iteration will be saved), "gap" (every' '`snapshot_gap` iterations are saved), or "none" ' '(do not save snapshots)') parser.add_argument('--snapshot_gap', type=int, default=1, help='Gap between snapshot iterations.') parser.add_argument('--tabular_log_file', type=str, default='progress.csv', help='Name of the tabular log file (in csv).') parser.add_argument('--text_log_file', type=str, default='debug.log', help='Name of the text log file (in pure text).') parser.add_argument('--params_log_file', type=str, default='params.json', help='Name of the parameter log file (in json).') parser.add_argument('--variant_log_file', type=str, default='variant.json', help='Name of the variant log file (in json).') parser.add_argument( '--resume_from', type=str, default=None, help='Name of the pickle file to resume experiment from.') parser.add_argument('--plot', type=ast.literal_eval, default=False, help='Whether to plot the iteration results') parser.add_argument( '--log_tabular_only', type=ast.literal_eval, default=False, help= 'Whether to only print the tabular log information (in a horizontal format)' ) parser.add_argument('--seed', type=int, help='Random seed for numpy') parser.add_argument('--args_data', type=str, help='Pickled data for stub objects') parser.add_argument('--variant_data', type=str, help='Pickled data for variant configuration') parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False) args = parser.parse_args(argv[1:]) if args.seed is not None: set_seed(args.seed) if args.n_parallel > 0: from rllab.sampler import parallel_sampler parallel_sampler.initialize(n_parallel=args.n_parallel) if args.seed is not None: parallel_sampler.set_seed(args.seed) if args.plot: from rllab.plotter import plotter plotter.init_worker() if args.log_dir is None: log_dir = osp.join(default_log_dir, args.exp_name) else: log_dir = args.log_dir tabular_log_file = osp.join(log_dir, args.tabular_log_file) text_log_file = osp.join(log_dir, args.text_log_file) params_log_file = osp.join(log_dir, args.params_log_file) if args.variant_data is not None: variant_data = pickle.loads(base64.b64decode(args.variant_data)) variant_log_file = osp.join(log_dir, args.variant_log_file) logger.log_variant(variant_log_file, variant_data) else: variant_data = None if not args.use_cloudpickle: logger.log_parameters_lite(params_log_file, args) logger.add_text_output(text_log_file) logger.add_tabular_output(tabular_log_file) prev_snapshot_dir = logger.get_snapshot_dir() prev_mode = logger.get_snapshot_mode() logger.set_snapshot_dir(log_dir) logger.set_snapshot_mode(args.snapshot_mode) logger.set_snapshot_gap(args.snapshot_gap) logger.set_log_tabular_only(args.log_tabular_only) logger.push_prefix("[%s] " % args.exp_name) #variant_data is the variant dictionary sent from trpoTests_ExpLite if (args.resume_from is not None) and ( '&|&' in args.resume_from ): #separate string on &|& to get iters and file location vals = args.resume_from.split( '&|&') #dirRes | numItrs to go | new batchSize dirRes = vals[0] numItrs = int(vals[1]) if (len(vals) > 2): batchSize = int(vals[2]) print("resuming from :{}".format(dirRes)) data = joblib.load(dirRes) #data is dict : 'baseline', 'algo', 'itr', 'policy', 'env' assert 'algo' in data algo = data['algo'] assert 'policy' in data pol = data['policy'] bl = data['baseline'] oldBatchSize = algo.batch_size algo.n_itr = numItrs if (len(vals) > 2): algo.batch_size = batchSize print( 'algo iters : {} cur iter :{} oldBatchSize : {} newBatchSize : {}' .format(algo.n_itr, algo.current_itr, oldBatchSize, algo.batch_size)) else: print('algo iters : {} cur iter :{} '.format( algo.n_itr, algo.current_itr)) algo.train() else: print('Not resuming - building new exp') # read from stdin if args.use_cloudpickle: #set to use cloudpickle import cloudpickle method_call = cloudpickle.loads(base64.b64decode(args.args_data)) method_call(variant_data) else: print('not use cloud pickle') data = pickle.loads(base64.b64decode(args.args_data)) maybe_iter = concretize(data) if is_iterable(maybe_iter): for _ in maybe_iter: pass logger.set_snapshot_mode(prev_mode) logger.set_snapshot_dir(prev_snapshot_dir) logger.remove_tabular_output(tabular_log_file) logger.remove_text_output(text_log_file) logger.pop_prefix()
def run_experiment(argv): default_log_dir = config.LOG_DIR now = datetime.datetime.now(dateutil.tz.tzlocal()) # avoid name clashes when running distributed jobs rand_id = str(uuid.uuid4())[:5] timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z') default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id) parser = argparse.ArgumentParser() parser.add_argument( '--n_parallel', type=int, default=1, help= 'Number of parallel workers to perform rollouts. 0 => don\'t start any workers' ) parser.add_argument('--exp_name', type=str, default=default_exp_name, help='Name of the experiment.') parser.add_argument('--log_dir', type=str, default=None, help='Path to save the log and iteration snapshot.') parser.add_argument('--snapshot_mode', type=str, default='all', help='Mode to save the snapshot. Can be either "all" ' '(all iterations will be saved), "last" (only ' 'the last iteration will be saved), "gap" (every' '`snapshot_gap` iterations are saved), or "none" ' '(do not save snapshots)') parser.add_argument('--snapshot_gap', type=int, default=1, help='Gap between snapshot iterations.') parser.add_argument('--tabular_log_file', type=str, default='progress.csv', help='Name of the tabular log file (in csv).') parser.add_argument('--text_log_file', type=str, default='debug.log', help='Name of the text log file (in pure text).') parser.add_argument('--tensorboard_log_dir', type=str, default='tb', help='Name of the folder for tensorboard_summary.') parser.add_argument( '--tensorboard_step_key', type=str, default=None, help= 'Name of the step key in log data which shows the step in tensorboard_summary.' ) parser.add_argument('--params_log_file', type=str, default='params.json', help='Name of the parameter log file (in json).') parser.add_argument('--variant_log_file', type=str, default='variant.json', help='Name of the variant log file (in json).') parser.add_argument( '--resume_from', type=str, default=None, help='Name of the pickle file to resume experiment from.') parser.add_argument('--plot', type=ast.literal_eval, default=False, help='Whether to plot the iteration results') parser.add_argument( '--log_tabular_only', type=ast.literal_eval, default=False, help= 'Whether to only print the tabular log information (in a horizontal format)' ) parser.add_argument('--seed', type=int, help='Random seed for numpy') parser.add_argument('--args_data', type=str, help='Pickled data for stub objects') parser.add_argument('--variant_data', type=str, help='Pickled data for variant configuration') parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False) parser.add_argument('--checkpoint_dir', type=str, default='checkpoint', help='Name of the folder for checkpoints.') parser.add_argument('--obs_dir', type=str, default='obs', help='Name of the folder for original observations.') args = parser.parse_args(argv[1:]) if args.seed is not None: set_seed(args.seed) if args.n_parallel > 0: from rllab.sampler import parallel_sampler parallel_sampler.initialize(n_parallel=args.n_parallel) if args.seed is not None: parallel_sampler.set_seed(args.seed) if args.plot: from rllab.plotter import plotter plotter.init_worker() if args.log_dir is None: log_dir = osp.join(default_log_dir, args.exp_name) else: log_dir = args.log_dir tabular_log_file = osp.join(log_dir, args.tabular_log_file) text_log_file = osp.join(log_dir, args.text_log_file) params_log_file = osp.join(log_dir, args.params_log_file) tensorboard_log_dir = osp.join(log_dir, args.tensorboard_log_dir) checkpoint_dir = osp.join(log_dir, args.checkpoint_dir) obs_dir = osp.join(log_dir, args.obs_dir) if args.variant_data is not None: variant_data = pickle.loads(base64.b64decode(args.variant_data)) variant_log_file = osp.join(log_dir, args.variant_log_file) logger.log_variant(variant_log_file, variant_data) else: variant_data = None if not args.use_cloudpickle: logger.log_parameters_lite(params_log_file, args) logger.add_text_output(text_log_file) logger.add_tabular_output(tabular_log_file) logger.set_tensorboard_dir(tensorboard_log_dir) logger.set_checkpoint_dir(checkpoint_dir) logger.set_obs_dir(obs_dir) prev_snapshot_dir = logger.get_snapshot_dir() prev_mode = logger.get_snapshot_mode() logger.set_snapshot_dir(log_dir) logger.set_snapshot_mode(args.snapshot_mode) logger.set_snapshot_gap(args.snapshot_gap) logger.set_log_tabular_only(args.log_tabular_only) logger.set_tensorboard_step_key(args.tensorboard_step_key) logger.push_prefix("[%s] " % args.exp_name) git_commit = get_git_commit_hash() logger.log('Git commit: {}'.format(git_commit)) git_diff_file_path = osp.join(log_dir, 'git_diff_{}.patch'.format(git_commit)) save_git_diff_to_file(git_diff_file_path) logger.log('hostname: {}, pid: {}, tmux session: {}'.format( socket.gethostname(), os.getpid(), get_tmux_session_name())) if args.resume_from is not None: data = joblib.load(args.resume_from) assert 'algo' in data algo = data['algo'] algo.train() else: # read from stdin if args.use_cloudpickle: import cloudpickle method_call = cloudpickle.loads(base64.b64decode(args.args_data)) method_call(variant_data) else: data = pickle.loads(base64.b64decode(args.args_data)) maybe_iter = concretize(data) if is_iterable(maybe_iter): for _ in maybe_iter: pass logger.set_snapshot_mode(prev_mode) logger.set_snapshot_dir(prev_snapshot_dir) logger.remove_tabular_output(tabular_log_file) logger.remove_text_output(text_log_file) logger.pop_prefix()
def run_experiment( args_data, variant_data=None, seed=None, n_parallel=1, exp_name=None, log_dir=None, snapshot_mode='all', snapshot_gap=1, tabular_log_file='progress.csv', text_log_file='debug.log', params_log_file='params.json', variant_log_file='variant.json', resume_from=None, plot=False, log_tabular_only=False, log_debug_log_only=False, ): default_log_dir = config.LOG_DIR now = datetime.datetime.now(dateutil.tz.tzlocal()) # avoid name clashes when running distributed jobs rand_id = str(uuid.uuid4())[:5] timestamp = now.strftime('%Y_%m_%d_%H_%M_%S_%f_%Z') default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id) if exp_name is None: exp_name = default_exp_name if seed is not None: set_seed(seed) if n_parallel > 0: from rllab.sampler import parallel_sampler parallel_sampler.initialize(n_parallel=n_parallel) if seed is not None: parallel_sampler.set_seed(seed) if plot: from rllab.plotter import plotter plotter.init_worker() if log_dir is None: log_dir = osp.join(default_log_dir, exp_name) else: log_dir = log_dir tabular_log_file = osp.join(log_dir, tabular_log_file) text_log_file = osp.join(log_dir, text_log_file) params_log_file = osp.join(log_dir, params_log_file) if variant_data is not None: variant_data = variant_data variant_log_file = osp.join(log_dir, variant_log_file) # print(variant_log_file) # print(variant_data) logger.log_variant(variant_log_file, variant_data) else: variant_data = None logger.add_text_output(text_log_file) logger.add_tabular_output(tabular_log_file) prev_snapshot_dir = logger.get_snapshot_dir() prev_mode = logger.get_snapshot_mode() logger.set_snapshot_dir(log_dir) logger.set_snapshot_mode(snapshot_mode) logger.set_snapshot_gap(snapshot_gap) logger.set_log_tabular_only(log_tabular_only) logger.set_debug_log_only(log_debug_log_only) logger.push_prefix("[%s] " % exp_name) if resume_from is not None: data = joblib.load(resume_from) assert 'algo' in data algo = data['algo'] algo.train() else: args_data(variant_data) logger.set_snapshot_mode(prev_mode) logger.set_snapshot_dir(prev_snapshot_dir) logger.remove_tabular_output(tabular_log_file) logger.remove_text_output(text_log_file) logger.pop_prefix()