Merge branch 'round-2' into 'master'

round 2 env with action repeats every 30ms

See merge request !1

.gitignore

 __pycache__
 deepracer-gym/deepracer_gym.egg-info/
-playground/
\ No newline at end of file
+playground/
+deepracer-gym/dump.py

deepracer-gym/deepracer_gym/envs/deepracer_gym_env.py

 import numpy as np
 import gym
 from deepracer_gym.zmq_client import DeepracerEnvHelper
-
+import time
+import warnings
 class DeepracerGymEnv(gym.Env):
     def __init__(self):
         self.action_space = gym.spaces.Discrete(5)
         self.deepracer_helper = DeepracerEnvHelper()
+        self.last_step_time = None
+        self.max_step_time = 0.03 # seconds
     
     def reset(self):
         observation = self.deepracer_helper.env_reset()
         return observation
     
     def step(self, action):
+        
+        if self.last_step_time is not None:
+            time_delta = time.time() - self.last_step_time - self.max_step_time
+        else:
+            time_delta = -1
+        done = False
+        n_repeats = 0
+
+        # This is to emulate async nature of the real world track
+        # If action is not returned within required time limit, the same action would be repeated
+        while (not done) and time_delta > 0:
+            time_delta -= self.max_step_time
+            observation, reward, done, info = self._step_sim(self.last_action)
+            n_repeats += 1
+        
+        if n_repeats > 0:
+            warn_msg = f"Action was repeated {n_repeats} times, try to reduce model step time to {self.max_step_time} seconds" 
+            warnings.warn(warn_msg)
+        
+        if not done:
+            observation, reward, done, info = self._step_sim(action)
+
+        self.last_action = action
+        self.last_step_time = time.time()
+        return observation, reward, done, info
+    
+    def _step_sim(self, action):
         rl_coach_obs = self.deepracer_helper.send_act_rcv_obs(action)
         observation, reward, done, info = self.deepracer_helper.unpack_rl_coach_obs(rl_coach_obs)
         return observation, reward, done, info

deepracer-gym/random_actions_example.py

@@ -2,6 +2,7 @@ import gym
 import numpy as np
 
 import deepracer_gym
+import time
 
 env = gym.make('deepracer_gym:deepracer-v0')
 
@@ -13,8 +14,11 @@ steps_completed = 0
 episodes_completed = 0
 total_reward = 0
 
-for _ in range(500):
+for i in range(500):
     observation, reward, done, info = env.step(np.random.randint(5))
+    # Example to show the delay in model inference 
+    if (i-5) % 5 == 0:
+        time.sleep(0.1)
   
     steps_completed += 1 
     total_reward += reward
@@ -23,4 +27,4 @@ for _ in range(500):
         episodes_completed += 1
         print("Episodes Completed:", episodes_completed, "Steps:", steps_completed, "Reward", total_reward)
         steps_completed = 0
-        total_reward = 0
\ No newline at end of file
+        total_reward = 0

deepracer-gym/start_deepracer_docker.sh

-docker run -it --rm --name=deepracer -p 8888:8888 -p 5000:5000 --cpus="3" --memory="6g" aicrowd/base-images:deepracer_round1_release /bin/bash
+docker run -it --rm --name=deepracer -p 8888:8888 -p 5000:5000 --cpus="3" --memory="6g" aicrowd/base-images:deepracer_round2_release /bin/bash