--- a
+++ b/rdpg/gym_rdpg.py
@@ -0,0 +1,63 @@
+from rdpg import *
+import opensim as osim
+from osim.http.client import Client
+from osim.env import *
+from history import History
+ENV_NAME = 'learning_to_run'
+PATH = 'models/'
+EPISODES = 100000
+TEST = 5
+
+def main():
+    env = RunEnv(visualize=False)
+    env.reset(difficulty = 0)
+    agent = RDPG(env)
+
+    returns = []
+    rewards = []
+
+    for episode in xrange(EPISODES):
+        state = env.reset(difficulty = 0)
+        reward_episode = []
+        print "episode:",episode
+        #Initializing empty history
+        history = History(state)
+        # Train
+        for step in xrange(env.spec.timestep_limit):
+            action = agent.noise_action(history)
+            next_state,reward,done,_ = env.step(action)
+            # appending to history
+            history.append(next_state,action,reward)
+            reward_episode.append(reward)
+            if done:
+                break
+        # storing the history into replay buffer and if the number of histories sequence is above the threshod, start training
+        agent.perceive(history)
+        # Testing:
+        #if episode % 1 == 0:
+        # if episode % 1000 == 0 and episode > 50:
+        #     agent.save_model(PATH, episode)
+
+        #     total_return = 0
+        #     ave_reward = 0
+        #     for i in xrange(TEST):
+        #         state = env.reset()
+        #         reward_per_step = 0
+        #         for j in xrange(env.spec.timestep_limit):
+        #             action = agent.action(state) # direct action for test
+        #             state,reward,done,_ = env.step(action)
+        #             total_return += reward
+        #             if done:
+        #                 break
+        #             reward_per_step += (reward - reward_per_step)/(j+1)
+        #         ave_reward += reward_per_step
+
+        #     ave_return = total_return/TEST
+        #     ave_reward = ave_reward/TEST
+        #     returns.append(ave_return)
+        #     rewards.append(ave_reward)
+
+        #     print 'episode: ',episode,'Evaluation Average Return:',ave_return, '  Evaluation Average Reward: ', ave_reward
+
+if __name__ == '__main__':
+    main()