Allow None max steps

.gitignore

 # Tensorflow Model Info
 /models
 /summaries
+/summaries_old
 
 # Training environments
 /envs

ml-agents/mlagents/trainers/agent_processor.py

 from typing import List, Dict
-from collections import defaultdict
+from collections import defaultdict, Counter
 import numpy as np
 
 from mlagents.trainers.trainer import Trainer
         # We still need some info from the policy (memories, previous actions)
         # that really should be gathered by the env-manager.
         self.policy = policy
-        self.episode_steps: Dict[str, int] = {}
-        self.max_trajectory_length = max_trajectory_length
+        self.episode_steps: Counter = Counter()
+        self.episode_rewards: Dict[str, float] = defaultdict(lambda: 0.0)
+        if max_trajectory_length:
+            self.max_trajectory_length = max_trajectory_length
+            self.ignore_max_length = False
+        else:
+            self.max_trajectory_length = 0
+            self.ignore_max_length = True
         self.trainer = trainer
 
     def add_experiences(
 
                 if (
                     next_info.local_done[next_idx]
-                    or len(self.experience_buffers[agent_id])
-                    >= self.max_trajectory_length
+                    or (
+                        not self.ignore_max_length
+                        and len(self.experience_buffers[agent_id])
+                        >= self.max_trajectory_length
+                    )
                 ) and len(self.experience_buffers[agent_id]) > 0:
                     # Make next AgentExperience
                     next_obs = []

ml-agents/mlagents/trainers/trainer_controller.py

                             processor=AgentProcessor(
                                 trainer,
                                 trainer.policy,
-                                trainer.parameters["time_horizon"],
+                                trainer.parameters["time_horizon"]
+                                if "time_horizon" in trainer.parameters
+                                else None,
+                                trainer.summary_path,
                             )
                         )
                         self.managers[name] = agent_manager