change requirement.txt

requirements.txt

@@ -6,6 +6,9 @@ astunparse==1.6.3
 atari-py==0.2.6
 base-local-planner==1.14.8
 bondpy==1.8.3
+Box2D==2.3.10
+Box2D-kengz==2.3.3
+box2d-py==2.3.8
 cachetools==4.1.1
 camera-calibration==1.12.23
 camera-calibration-parsers==1.11.13
@@ -45,15 +48,17 @@ importlib-metadata==2.0.0
 interactive-markers==1.11.5
 joblib==0.17.0
 joint-state-publisher==1.12.15
+Keras==2.4.3
 Keras-Applications==1.0.8
 Keras-Preprocessing==1.1.2
 kiwisolver==1.2.0
 laser-geometry==1.6.5
 Markdown==3.3.3
 matplotlib==3.3.2
 message-filters==1.12.16
+mock==4.0.2
 mpi4py==3.0.3
-numpy==1.18.5
+numpy==1.16.0
 oauthlib==3.1.0
 opencv-python==4.4.0.44
 opt-einsum==3.3.0
@@ -63,12 +68,14 @@ pluginlib==1.11.3
 protobuf==3.13.0
 pyasn1==0.4.8
 pyasn1-modules==0.2.8
+pybullet==3.0.6
 pygame==1.9.6
 pyglet==1.5.0
 pyparsing==2.4.7
 python-dateutil==2.8.1
 python-qt-binding==0.3.7
 pytz==2020.1
+PyYAML==5.3.1
 qt-dotgraph==0.3.17
 qt-gui==0.3.17
 qt-gui-cpp==0.3.17
@@ -139,11 +146,11 @@ smach==2.0.1
 smach-ros==2.0.1
 smclib==1.8.3
 stable-baselines==2.10.1
-tensorboard==1.14.0
+tensorboard==1.13.1
 tensorboard-plugin-wit==1.7.0
 tensorboardX==2.1
-tensorflow==1.14.0
-tensorflow-estimator==1.14.0
+tensorflow==1.13.1
+tensorflow-estimator==1.13.0
 termcolor==1.1.0
 tf==1.11.9
 tf-conversions==1.11.9

test_virtual/PPO2_overtaking.py

@@ -0,0 +1,48 @@
+import os
+
+import gym
+import highway_env
+import pybullet_envs
+
+from stable_baselines.common.vec_env import DummyVecEnv, VecNormalize
+from stable_baselines import PPO2
+
+env = DummyVecEnv([lambda: gym.make("overtaking-v0")])
+# Automatically normalize the input features and reward
+env = VecNormalize(env, norm_obs=True, norm_reward=True,
+                   clip_obs=10.)
+
+
+model = PPO2('MlpPolicy', env)
+model.learn(total_timesteps=2000)
+
+obs = env.reset()
+for i in range(10):
+    action, _states = model.predict(obs)
+    obs, rewards, dones, info = env.step(action)
+    env.render()
+
+#  do not update them at test time
+env.training = False
+# reward normalization is not needed at test time
+env.norm_reward = False
+
+# Don't forget to save the VecNormalize statistics when saving the agent
+log_dir = "/home/cxc/下载/实验结果"
+model.save(log_dir + "ppo_overtaking")
+stats_path = os.path.join(log_dir, "vec_normalize.pkl")
+env.save(stats_path)
+
+# To demonstrate loading
+del model, env
+
+# Load the agent
+model = PPO2.load(log_dir + "ppo_overtaking")
+
+
+
+# Load the saved statistics
+# env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
+# env = VecNormalize.load(stats_path, env)
+
+