Add nearly all gym environments using pybullet together with the latest tf model from the roboschool model zoo.

examples/pybullet/gym/envs/kerasrl_utils.py

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+
+import re
+from gym import error
+import glob
+							#  checkpoints/KerasDDPG-InvertedPendulum-v0-20170701190920_actor.h5
+weight_save_re = re.compile(r'^(?:\w+\/)+?(\w+-v\d+)-(\w+-v\d+)-(\d+)(?:_\w+)?\.(\w+)$')
+
+def get_fields(weight_save_name):
+	match = weight_save_re.search(weight_save_name)
+	if not match:
+		raise error.Error('Attempted to read a malformed weight save: {}. (Currently all weight saves must be of the form {}.)'.format(id,weight_save_re.pattern))
+	return match.group(1), match.group(2), int(match.group(3))
+
+def get_latest_save(file_folder, agent_name, env_name, version_number):
+	"""
+	Returns the properties of the latest weight save. The information can be used to generate the loading path
+	:return:
+	"""
+	path = "%s%s"% (file_folder, "*.h5")
+	file_list = glob.glob(path)
+	latest_file_properties = []
+	file_properties = []
+	for f in file_list:
+		file_properties = get_fields(f)
+		if file_properties[0] == agent_name and file_properties[1] == env_name and (latest_file_properties == [] or file_properties[2] > latest_file_properties[2]):
+			latest_file_properties = file_properties
+
+	return latest_file_properties