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https://github.com/gryf/coach.git
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Release 0.9
Main changes are detailed below: New features - * CARLA 0.7 simulator integration * Human control of the game play * Recording of human game play and storing / loading the replay buffer * Behavioral cloning agent and presets * Golden tests for several presets * Selecting between deep / shallow image embedders * Rendering through pygame (with some boost in performance) API changes - * Improved environment wrapper API * Added an evaluate flag to allow convenient evaluation of existing checkpoints * Improve frameskip definition in Gym Bug fixes - * Fixed loading of checkpoints for agents with more than one network * Fixed the N Step Q learning agent python3 compatibility
This commit is contained in:
Itai Caspi
86
coach.py
86
coach.py
@@ -37,8 +37,29 @@ time_started = datetime.datetime.now()
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cur_time = time_started.time()
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cur_date = time_started.date()
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def get_experiment_path(general_experiments_path):
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if not os.path.exists(general_experiments_path):
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def get_experiment_name(initial_experiment_name=''):
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match = None
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while match is None:
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if initial_experiment_name == '':
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experiment_name = screen.ask_input("Please enter an experiment name: ")
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else:
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experiment_name = initial_experiment_name
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experiment_name = experiment_name.replace(" ", "_")
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match = re.match("^$|^[\w -/]{1,100}$", experiment_name)
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if match is None:
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screen.error('Experiment name must be composed only of alphanumeric letters, '
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'underscores and dashes and should not be longer than 100 characters.')
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return match.group(0)
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def get_experiment_path(experiment_name, create_path=True):
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general_experiments_path = os.path.join('./experiments/', experiment_name)
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if not os.path.exists(general_experiments_path) and create_path:
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os.makedirs(general_experiments_path)
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experiment_path = os.path.join(general_experiments_path, '{}_{}_{}-{}_{}'
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.format(logger.two_digits(cur_date.day), logger.two_digits(cur_date.month),
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@@ -52,7 +73,8 @@ def get_experiment_path(general_experiments_path):
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cur_time.minute, i))
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i += 1
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else:
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os.makedirs(experiment_path)
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if create_path:
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os.makedirs(experiment_path)
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return experiment_path
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@@ -96,55 +118,54 @@ def check_input_and_fill_run_dict(parser):
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num_workers = int(re.match("^\d+$", args.num_workers).group(0))
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except ValueError:
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screen.error("Parameter num_workers should be an integer.")
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exit(1)
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preset_names = list_all_classes_in_module(presets)
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if args.preset is not None and args.preset not in preset_names:
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screen.error("A non-existing preset was selected. ")
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exit(1)
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if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
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screen.error("The requested checkpoint folder to load from does not exist. ")
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exit(1)
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if args.save_model_sec is not None:
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try:
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args.save_model_sec = int(args.save_model_sec)
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except ValueError:
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screen.error("Parameter save_model_sec should be an integer.")
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exit(1)
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if args.preset is None and (args.agent_type is None or args.environment_type is None
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or args.exploration_policy_type is None):
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or args.exploration_policy_type is None) and not args.play:
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screen.error('When no preset is given for Coach to run, the user is expected to input the desired agent_type,'
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' environment_type and exploration_policy_type to assemble a preset. '
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'\nAt least one of these parameters was not given.')
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exit(1)
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elif args.preset is None and args.play and args.environment_type is None:
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screen.error('When no preset is given for Coach to run, and the user requests human control over the environment,'
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' the user is expected to input the desired environment_type and level.'
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'\nAt least one of these parameters was not given.')
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elif args.preset is None and args.play and args.environment_type:
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args.agent_type = 'Human'
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args.exploration_policy_type = 'ExplorationParameters'
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experiment_name = args.experiment_name
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# get experiment name and path
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experiment_name = get_experiment_name(args.experiment_name)
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experiment_path = get_experiment_path(experiment_name)
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if args.experiment_name == '':
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experiment_name = screen.ask_input("Please enter an experiment name: ")
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experiment_name = experiment_name.replace(" ", "_")
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match = re.match("^$|^\w{1,100}$", experiment_name)
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if match is None:
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screen.error('Experiment name must be composed only of alphanumeric letters and underscores and should not be '
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'longer than 100 characters.')
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exit(1)
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experiment_path = os.path.join('./experiments/', match.group(0))
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experiment_path = get_experiment_path(experiment_path)
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if args.play and num_workers > 1:
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screen.warning("Playing the game as a human is only available with a single worker. "
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"The number of workers will be reduced to 1")
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num_workers = 1
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# fill run_dict
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run_dict = dict()
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run_dict['agent_type'] = args.agent_type
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run_dict['environment_type'] = args.environment_type
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run_dict['exploration_policy_type'] = args.exploration_policy_type
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run_dict['level'] = args.level
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run_dict['preset'] = args.preset
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run_dict['custom_parameter'] = args.custom_parameter
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run_dict['experiment_path'] = experiment_path
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run_dict['framework'] = Frameworks().get(args.framework)
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run_dict['play'] = args.play
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run_dict['evaluate'] = args.evaluate# or args.play
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# multi-threading parameters
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run_dict['num_threads'] = num_workers
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@@ -197,6 +218,14 @@ if __name__ == "__main__":
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help="(int) Number of workers for multi-process based agents, e.g. A3C",
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default='1',
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type=str)
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parser.add_argument('--play',
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help="(flag) Play as a human by controlling the game with the keyboard. "
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"This option will save a replay buffer with the game play.",
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action='store_true')
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parser.add_argument('--evaluate',
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help="(flag) Run evaluation only. This is a convenient way to disable "
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"training in order to evaluate an existing checkpoint.",
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action='store_true')
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parser.add_argument('-v', '--verbose',
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help="(flag) Don't suppress TensorFlow debug prints.",
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action='store_true')
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@@ -230,6 +259,12 @@ if __name__ == "__main__":
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,
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default=None,
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type=str)
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parser.add_argument('-lvl', '--level',
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help="(string) Choose the level that will be played in the environment that was selected."
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"This value will override the level parameter in the environment class."
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,
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default=None,
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type=str)
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parser.add_argument('-cp', '--custom_parameter',
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help="(string) Semicolon separated parameters used to override specific parameters on top of"
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" the selected preset (or on top of the command-line assembled one). "
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@@ -259,7 +294,12 @@ if __name__ == "__main__":
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tuning_parameters.task_index = 0
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env_instance = create_environment(tuning_parameters)
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agent = eval(tuning_parameters.agent.type + '(env_instance, tuning_parameters)')
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agent.improve()
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# Start the training or evaluation
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if tuning_parameters.evaluate:
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agent.evaluate(sys.maxsize, keep_networks_synced=True) # evaluate forever
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else:
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agent.improve()
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# Multi-threaded runs
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else:
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