Policy updated

reinforcement_learning/deadlockavoidance_with_decision_agent.py

@@ -10,6 +10,8 @@ from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
 class DeadLockAvoidanceWithDecisionAgent(HybridPolicy):
 
     def __init__(self, env: RailEnv, state_size, action_size, learning_agent):
+        print(">> DeadLockAvoidanceWithDecisionAgent")
+        super(DeadLockAvoidanceWithDecisionAgent, self).__init__()
         self.env = env
         self.state_size = state_size
         self.action_size = action_size
@@ -33,7 +35,7 @@ class DeadLockAvoidanceWithDecisionAgent(HybridPolicy):
         if agent.status < RailAgentStatus.DONE:
             agents_on_switch, agents_near_to_switch, _, _ = \
                 self.agent_can_choose_helper.check_agent_decision(position, direction)
-            if agents_on_switch:
+            if agents_on_switch or agents_near_to_switch:
                 return self.learning_agent.act(handle, state, eps)
             else:
                 act = self.dead_lock_avoidance_agent.act(handle, state, -1.0)

reinforcement_learning/multi_agent_training.py

@@ -177,12 +177,16 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
         policy = PPOPolicy(state_size, get_action_size(), use_replay_buffer=False, in_parameters=train_params)
     if False:
         policy = DeadLockAvoidanceAgent(train_env, get_action_size())
-    if False:
-        inter_policy = PPOPolicy(state_size, get_action_size(), use_replay_buffer=False, in_parameters=train_params)
-        policy = DeadLockAvoidanceWithDecisionAgent(train_env, state_size, get_action_size(), inter_policy)
     if True:
+        inter_policy = DDDQNPolicy(state_size, get_action_size(), train_params)
+        policy = DeadLockAvoidanceWithDecisionAgent(train_env, state_size, get_action_size(), inter_policy)
+    if False:
         policy = MultiDecisionAgent(state_size, get_action_size(), train_params)
 
+    # make sure that at least one policy is set
+    if policy is None:
+        policy = DDDQNPolicy(state_size, get_action_size(), train_params)
+
     # Load existing policy
     if train_params.load_policy is not "":
         policy.load(train_params.load_policy)

utils/dead_lock_avoidance_agent.py

@@ -6,7 +6,7 @@ from flatland.core.env_observation_builder import DummyObservationBuilder
 from flatland.envs.agent_utils import RailAgentStatus
 from flatland.envs.rail_env import RailEnv, RailEnvActions, fast_count_nonzero
 
-from reinforcement_learning.policy import HeuristicPolicy
+from reinforcement_learning.policy import HeuristicPolicy, DummyMemory
 from utils.agent_action_config import map_rail_env_action
 from utils.shortest_distance_walker import ShortestDistanceWalker
 
@@ -66,10 +66,6 @@ class DeadlockAvoidanceShortestDistanceWalker(ShortestDistanceWalker):
                 self.shortest_distance_agent_map[(handle, position[0], position[1])] = 1
         self.full_shortest_distance_agent_map[(handle, position[0], position[1])] = 1
 
-
-
-
-
 class DeadLockAvoidanceAgent(HeuristicPolicy):
     def __init__(self, env: RailEnv, action_size, show_debug_plot=False):
         self.env = env