add ARS to train/eval Minitaur

examples/pybullet/gym/pybullet_envs/ARS/eval_ars.py

@@ -0,0 +1,99 @@
+"""
+blaze run -c opt //experimental/users/jietan/ARS:eval_ars -- \
+--logdir=/cns/ij-d/home/jietan/experiment/ARS/ars_react_nr01.191950338.191950550/ \
+--checkpoint=lin_policy_plus_990.npz \
+--num_rollouts=10
+"""
+
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os, inspect
+import time
+
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+os.sys.path.insert(0,currentdir)
+
+from absl import app
+from absl import flags
+
+import pdb
+import os
+import numpy as np
+import gym
+import config_ars
+import utility
+import policies
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('logdir', None, 'The path of the checkpoint.')
+flags.DEFINE_string('checkpoint', None, 'The file name of the checkpoint.')
+flags.DEFINE_integer('num_rollouts', 1, 'The number of rollouts.')
+
+
+def main(argv):
+  del argv  # Unused.
+
+  print('loading and building expert policy')
+  checkpoint_file = os.path.join(FLAGS.logdir, FLAGS.checkpoint)
+  lin_policy = np.load(checkpoint_file, encoding='bytes')
+  lin_policy = lin_policy.items()[0][1]
+
+  M = lin_policy[0]
+  # mean and std of state vectors estimated online by ARS.
+  mean = lin_policy[1]
+  std = lin_policy[2]
+
+  config = utility.load_config(FLAGS.logdir)
+  print("config=",config)
+  env = config['env'](hard_reset=True, render=True)
+  ob_dim = env.observation_space.shape[0]
+  ac_dim = env.action_space.shape[0]
+
+  # set policy parameters. Possible filters: 'MeanStdFilter' for v2, 'NoFilter' for v1.
+  policy_params = {
+      'type': 'linear',
+      'ob_filter': config['filter'],
+      'ob_dim': ob_dim,
+      'ac_dim': ac_dim,
+      "weights": M,
+      "mean": mean,
+      "std": std,
+  }
+  policy = policies.LinearPolicy(policy_params, update_filter=False)
+  returns = []
+  observations = []
+  actions = []
+  for i in range(FLAGS.num_rollouts):
+    print('iter', i)
+    obs = env.reset()
+    done = False
+    totalr = 0.
+    steps = 0
+    while not done:
+      action = policy.act(obs)
+      observations.append(obs)
+      actions.append(action)
+
+      obs, r, done, _ = env.step(action)
+      time.sleep(1./100.)
+      totalr += r
+      steps += 1
+      if steps % 100 == 0:
+        print('%i/%i' % (steps, config['rollout_length']))
+      if steps >= config['rollout_length']:
+        break
+    returns.append(totalr)
+
+  print('returns', returns)
+  print('mean return', np.mean(returns))
+  print('std of return', np.std(returns))
+
+
+if __name__ == '__main__':
+  flags.mark_flag_as_required('logdir')
+  flags.mark_flag_as_required('checkpoint')
+  app.run(main)