fix gym/envs/bullet/cartpole_bullet.py (velocity hyperparameter still needs to be tuned)

add enjoy_pybullet_cartpole.py

examples/pybullet/gym/enjoy_pybullet_cartpole.py

@@ -0,0 +1,29 @@
+import gym
+
+from baselines import deepq
+from envs.bullet.cartpole_bullet import CartPoleBulletEnv
+
+def main():
+    env = gym.make('CartPoleBulletEnv-v0')
+    act = deepq.load("cartpole_model.pkl")
+
+    while True:
+        obs, done = env.reset(), False
+        print("obs")
+        print(obs)
+        print("type(obs)")
+        print(type(obs))
+        episode_rew = 0
+        while not done:
+            env.render()
+           
+            o = obs[None]
+            aa = act(o)
+            a = aa[0]
+            obs, rew, done, _ = env.step(a)
+            episode_rew += rew
+        print("Episode reward", episode_rew)
+
+
+if __name__ == '__main__':
+    main()

examples/pybullet/gym/envs/bullet/cartpole_bullet.py

@@ -25,7 +25,7 @@ class CartPoleBulletEnv(gym.Env):
   def __init__(self):
     # start the bullet physics server
     p.connect(p.GUI)
-#    p.connect(p.DIRECT)
+    #p.connect(p.DIRECT)
     observation_high = np.array([
           np.finfo(np.float32).max,
           np.finfo(np.float32).max,
@@ -33,7 +33,7 @@ class CartPoleBulletEnv(gym.Env):
           np.finfo(np.float32).max])
     action_high = np.array([0.1])
 
-    self.action_space = spaces.Box(-action_high, action_high)
+    self.action_space = spaces.Discrete(5)
     self.observation_space = spaces.Box(-observation_high, observation_high)
 
     self.theta_threshold_radians = 1
@@ -55,8 +55,11 @@ class CartPoleBulletEnv(gym.Env):
 #    time.sleep(self.timeStep)
     self.state = p.getJointState(self.cartpole, 1)[0:2] + p.getJointState(self.cartpole, 0)[0:2]
     theta, theta_dot, x, x_dot = self.state
-    force = action
-    p.setJointMotorControl2(self.cartpole, 0, p.VELOCITY_CONTROL, targetVelocity=(action + self.state[3]))
+    
+    dv = 0.4
+    deltav = [-2.*dv, -dv, 0, dv, 2.*dv][action]
+    
+    p.setJointMotorControl2(self.cartpole, 0, p.VELOCITY_CONTROL, targetVelocity=(deltav + self.state[3]))
 
     done =  x < -self.x_threshold \
                 or x > self.x_threshold \