Spaces:
Sleeping
Sleeping
| """ | |
| Common evaluation utilities. | |
| """ | |
| from collections import OrderedDict | |
| from numbers import Number | |
| import numpy as np | |
| import src.rlkit.pythonplusplus as ppp | |
| def get_generic_path_information(paths, stat_prefix=''): | |
| """ | |
| Get an OrderedDict with a bunch of statistic names and values. | |
| """ | |
| statistics = OrderedDict() | |
| returns = [sum(path["rewards"]) for path in paths] | |
| rewards = np.vstack([path["rewards"] for path in paths]) | |
| statistics.update(create_stats_ordered_dict('Rewards', rewards, | |
| stat_prefix=stat_prefix)) | |
| statistics.update(create_stats_ordered_dict('Returns', returns, | |
| stat_prefix=stat_prefix)) | |
| actions = [path["actions"] for path in paths] | |
| if len(actions[0].shape) == 1: | |
| actions = np.hstack([path["actions"] for path in paths]) | |
| else: | |
| actions = np.vstack([path["actions"] for path in paths]) | |
| statistics.update(create_stats_ordered_dict( | |
| 'Actions', actions, stat_prefix=stat_prefix | |
| )) | |
| statistics['Num Paths'] = len(paths) | |
| statistics[stat_prefix + 'Average Returns'] = get_average_returns(paths) | |
| for info_key in ['env_infos', 'agent_infos']: | |
| if info_key in paths[0]: | |
| all_env_infos = [ | |
| ppp.list_of_dicts__to__dict_of_lists(p[info_key]) | |
| for p in paths | |
| ] | |
| for k in all_env_infos[0].keys(): | |
| final_ks = np.array([info[k][-1] for info in all_env_infos]) | |
| first_ks = np.array([info[k][0] for info in all_env_infos]) | |
| all_ks = np.concatenate([info[k] for info in all_env_infos]) | |
| statistics.update(create_stats_ordered_dict( | |
| stat_prefix + k, | |
| final_ks, | |
| stat_prefix='{}/final/'.format(info_key), | |
| )) | |
| statistics.update(create_stats_ordered_dict( | |
| stat_prefix + k, | |
| first_ks, | |
| stat_prefix='{}/initial/'.format(info_key), | |
| )) | |
| statistics.update(create_stats_ordered_dict( | |
| stat_prefix + k, | |
| all_ks, | |
| stat_prefix='{}/'.format(info_key), | |
| )) | |
| return statistics | |
| def get_average_returns(paths): | |
| returns = [sum(path["rewards"]) for path in paths] | |
| return np.mean(returns) | |
| def create_stats_ordered_dict( | |
| name, | |
| data, | |
| stat_prefix=None, | |
| always_show_all_stats=True, | |
| exclude_max_min=False, | |
| ): | |
| if stat_prefix is not None: | |
| name = "{}{}".format(stat_prefix, name) | |
| if isinstance(data, Number): | |
| return OrderedDict({name: data}) | |
| if len(data) == 0: | |
| return OrderedDict() | |
| if isinstance(data, tuple): | |
| ordered_dict = OrderedDict() | |
| for number, d in enumerate(data): | |
| sub_dict = create_stats_ordered_dict( | |
| "{0}_{1}".format(name, number), | |
| d, | |
| ) | |
| ordered_dict.update(sub_dict) | |
| return ordered_dict | |
| if isinstance(data, list): | |
| try: | |
| iter(data[0]) | |
| except TypeError: | |
| pass | |
| else: | |
| data = np.concatenate(data) | |
| if (isinstance(data, np.ndarray) and data.size == 1 | |
| and not always_show_all_stats): | |
| return OrderedDict({name: float(data)}) | |
| stats = OrderedDict([ | |
| (name + ' Mean', np.mean(data)), | |
| (name + ' Std', np.std(data)), | |
| ]) | |
| if not exclude_max_min: | |
| stats[name + ' Max'] = np.max(data) | |
| stats[name + ' Min'] = np.min(data) | |
| return stats | |