add actuatornet code

examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/proto2csv.py

@@ -0,0 +1,46 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+#python proto2csv.py --proto_file=/tmp/logs/minitaur_log_2019-01-27-12-59-31 --csv_file=/tmp/logs/out.csv
+#each line in csv contains: angle, velocity, action, torque
+
+import tensorflow as tf
+import argparse
+import numpy
+from pybullet_envs.minitaur.envs import minitaur_logging
+
+flags = tf.app.flags
+FLAGS = tf.app.flags.FLAGS
+
+flags.DEFINE_string("proto_file", "logs", "path to protobuf input file")
+flags.DEFINE_string("csv_file", "file.csv", "poth to csv output file")
+
+
+def main(argv):
+  del argv
+
+  logging = minitaur_logging.MinitaurLogging()
+  episode = logging.restore_episode(FLAGS.proto_file)
+  #print(dir (episode))
+  #print("episode=",episode)
+  fields = episode.ListFields()
+
+  recs = []
+
+  for rec in fields[0][1]:
+    #print(rec.time)
+    for motorState in rec.motor_states:
+      #print("motorState.angle=",motorState.angle)
+      #print("motorState.velocity=",motorState.velocity)
+      #print("motorState.action=",motorState.action)
+      #print("motorState.torque=",motorState.torque)
+      recs.append([motorState.angle,motorState.velocity,motorState.action,motorState.torque])
+		
+  a = numpy.array(recs)
+  numpy.savetxt(FLAGS.csv_file, a, delimiter=",")
+
+if __name__ == "__main__":
+  tf.app.run(main)
+
+