Added a single process environment runner. Also added an example for using such class.

rltorch/env/simulate.py

@@ -1,3 +1,6 @@
+from copy import deepcopy 
+import rltorch
+
 def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger = None, name = ""):
   for episode in range(total_episodes):
     state = env.reset()
@@ -19,3 +22,44 @@ def simulateEnvEps(env, actor, config, total_episodes = 1, memory = None, logger
     if logger is not None:
       logger.append(name + '/EpisodeReward', episode_reward)
 
+
+class EnvironmentRunSync():
+  def __init__(self, env, actor, config, memory = None, logwriter = None, name = ""):
+    self.env = env
+    self.name = name
+    self.actor = actor
+    self.config = deepcopy(config)
+    self.logwriter = logwriter
+    self.memory = memory
+    self.episode_num = 1
+    self.episode_reward = 0
+    self.last_state = env.reset()
+
+  def run(self, iterations):
+    state = self.last_state
+    logger = rltorch.log.Logger() if self.logwriter is not None else None
+    for _ in range(iterations):
+      action = self.actor.act(state)
+      next_state, reward, done, _ = self.env.step(action)
+       
+      self.episode_reward += reward
+      if self.memory is not None:
+        self.memory.append(state, action, reward, next_state, done)
+       
+      state = next_state
+
+      if done:
+        if self.episode_num % self.config['print_stat_n_eps'] == 0:
+          print("episode: {}/{}, score: {}"
+            .format(self.episode_num, self.config['total_training_episodes'], self.episode_reward))
+          
+        if self.logwriter is not None:
+          logger.append(self.name + '/EpisodeReward', self.episode_reward)
+        self.episode_reward = 0
+        state = self.env.reset()
+        self.episode_num +=  1
+          
+    if self.logwriter is not None:
+      self.logwriter.write(logger)
+    
+    self.last_state = state