From 63663b73c221fabf62430c8dfbcb17ebf37ad098 Mon Sep 17 00:00:00 2001
From: Divye Pratap Jain <divye@D-10-18-171-180.dhcp4.washington.edu>
Date: Fri, 17 May 2019 15:07:27 -0700
Subject: [PATCH 1/2] added evalua_policy_vil in gym_env
---
mjrl/utils/gym_env.py | 76 +++++++++++++++++++++++++++++++++++++++++++
1 file changed, 76 insertions(+)
diff --git a/mjrl/utils/gym_env.py b/mjrl/utils/gym_env.py
index 9061b05..de575b5 100644
--- a/mjrl/utils/gym_env.py
+++ b/mjrl/utils/gym_env.py
@@ -1,5 +1,6 @@
import gym
import numpy as np
+import tqdm
class EnvSpec(object):
def __init__(self, obs_dim, act_dim, horizon, num_agents):
@@ -79,6 +80,7 @@ def evaluate_policy(self, policy,
if seed is not None:
self.env._seed(seed)
horizon = self._horizon if horizon is None else horizon
+
mean_eval, std, min_eval, max_eval = 0.0, 0.0, -1e8, -1e8
ep_returns = np.zeros(num_episodes)
@@ -123,3 +125,77 @@ def evaluate_policy(self, policy,
full_dist = ep_returns
return [base_stats, percentile_stats, full_dist]
+
+ def evaluate_policy_vil(self, policy,
+ use_tactile,
+ num_episodes=5,
+ horizon=None,
+ gamma=1,
+ percentile=[],
+ get_full_dist=False,
+ mean_action=False,
+ terminate_at_done=True,
+ seed=None,
+ camera_name=None,
+ device_id=0,
+ use_cuda=False,
+ frame_size=(128, 128)):
+
+ if seed is not None:
+ self.env.env._seed(seed)
+ np.random.seed(seed)
+
+ horizon = self._horizon if horizon is None else horizon
+ mean_eval, std, min_eval, max_eval = 0.0, 0.0, -1e8, -1e8
+ ep_returns = np.zeros(num_episodes)
+
+ for ep in tqdm(range(num_episodes)):
+
+ self.reset()
+ robot_info = self.env.env.get_proprioception(use_tactile=use_tactile)
+
+ path_image_pixels = []
+ t, done = 0, False
+ while t < horizon and not (done and terminate_at_done):
+ image_pix = self.get_pixels(frame_size=frame_size, camera_name=camera_name, device_id=device_id)
+ img = image_pix
+ prev_img = image_pix
+ prev_prev_img = image_pix
+ if t > 0:
+ prev_img = path_image_pixels[t - 1]
+
+ if t > 1:
+ prev_prev_img = path_image_pixels[t - 2]
+ path_image_pixels.append(img)
+ prev_prev_img = np.expand_dims(prev_prev_img, axis=0)
+ prev_img = np.expand_dims(prev_img, axis=0)
+ img = np.expand_dims(img, axis=0)
+
+ o = np.concatenate((prev_prev_img, prev_img, img), axis=0)
+
+ if mean_action:
+ a = policy.get_action(o, robot_info=robot_info, use_cuda=use_cuda)[1]['mean']
+ else:
+ a = policy.get_action(o, robot_info=robot_info, use_cuda=use_cuda)[0]
+
+ o, r, done, _ = self.step(a)
+
+ robot_info = self.env.env.get_proprioception(use_tactile=use_tactile)
+
+ ep_returns[ep] += (gamma ** t) * r
+ t += 1
+
+ mean_eval, std = np.mean(ep_returns), np.std(ep_returns)
+ min_eval, max_eval = np.amin(ep_returns), np.amax(ep_returns)
+ base_stats = [mean_eval, std, min_eval, max_eval]
+
+ percentile_stats = []
+ full_dist = []
+
+ for p in percentile:
+ percentile_stats.append(np.percentile(ep_returns, p))
+
+ if get_full_dist == True:
+ full_dist = ep_returns
+
+ return [base_stats, percentile_stats, full_dist]
From 7b32d4d342f55cb6a9834c7b2426d26812def450 Mon Sep 17 00:00:00 2001
From: Divye Pratap Jain <divye@D-10-18-171-180.dhcp4.washington.edu>
Date: Fri, 17 May 2019 22:29:23 -0700
Subject: [PATCH 2/2] NIT to gym_env
---
mjrl/utils/gym_env.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/mjrl/utils/gym_env.py b/mjrl/utils/gym_env.py
index de575b5..eac628c 100644
--- a/mjrl/utils/gym_env.py
+++ b/mjrl/utils/gym_env.py
@@ -1,6 +1,6 @@
import gym
import numpy as np
-import tqdm
+from tqdm import tqdm
class EnvSpec(object):
def __init__(self, obs_dim, act_dim, horizon, num_agents):
@@ -157,7 +157,7 @@ def evaluate_policy_vil(self, policy,
path_image_pixels = []
t, done = 0, False
while t < horizon and not (done and terminate_at_done):
- image_pix = self.get_pixels(frame_size=frame_size, camera_name=camera_name, device_id=device_id)
+ image_pix = self.env.env.get_pixels(frame_size=frame_size, camera_name=camera_name, device_id=device_id)
img = image_pix
prev_img = image_pix
prev_prev_img = image_pix