add ARS to train/eval Minitaur

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

@@ -0,0 +1,72 @@
+"""
+Policy class for computing action from weights and observation vector.
+Horia Mania --- hmania@berkeley.edu
+Aurelia Guy
+Benjamin Recht
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import filter
+
+
+class Policy(object):
+
+  def __init__(self, policy_params):
+
+    self.ob_dim = policy_params['ob_dim']
+    self.ac_dim = policy_params['ac_dim']
+    self.weights = np.empty(0)
+
+    # a filter for updating statistics of the observations and normalizing
+    # inputs to the policies
+    self.observation_filter = filter.get_filter(
+        policy_params['ob_filter'], shape=(self.ob_dim,))
+    self.update_filter = True
+
+  def update_weights(self, new_weights):
+    self.weights[:] = new_weights[:]
+    return
+
+  def get_weights(self):
+    return self.weights
+
+  def get_observation_filter(self):
+    return self.observation_filter
+
+  def act(self, ob):
+    raise NotImplementedError
+
+  def copy(self):
+    raise NotImplementedError
+
+
+class LinearPolicy(Policy):
+  """
+    Linear policy class that computes action as <w, ob>.
+    """
+
+  def __init__(self, policy_params, update_filter=True):
+    Policy.__init__(self, policy_params)
+    self.weights = np.zeros(self.ac_dim * self.ob_dim, dtype=np.float64)
+    if "weights" in policy_params:
+      self.weights = policy_params["weights"]
+    if "mean" in policy_params:
+      self.observation_filter.mean = policy_params["mean"]
+    if "std" in policy_params:
+      self.observation_filter.std = policy_params["std"]
+    self.update_filter = update_filter
+
+  def act(self, ob):
+    ob = self.observation_filter(ob, update=self.update_filter)
+    matrix_weights = np.reshape(self.weights, (self.ac_dim, self.ob_dim))
+    return np.clip(np.dot(matrix_weights, ob), -1.0, 1.0)
+
+  def get_weights_plus_stats(self):
+
+    mu, std = self.observation_filter.get_stats()
+    aux = np.asarray([self.weights, mu, std])
+    return aux