diff --git a/reinforcement_learning/multi_agent_training.py b/reinforcement_learning/multi_agent_training.py
index dc4fc33f0e8397f4610a40a633e660b73cb6c99c..542f587b1c3bea557c5d9e5f90e6210415fcf4a7 100644
--- a/reinforcement_learning/multi_agent_training.py
+++ b/reinforcement_learning/multi_agent_training.py
@@ -9,6 +9,7 @@ from pprint import pprint
import numpy as np
import psutil
+from flatland.envs.agent_utils import RailAgentStatus
from flatland.envs.malfunction_generators import malfunction_from_params, MalfunctionParameters
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
@@ -196,8 +197,6 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params, clo
policy = PPOAgent(state_size, action_size, n_agents, train_env)
if False:
policy = MultiPolicy(state_size, action_size, n_agents, train_env)
- if False:
- policy = DeadLockAvoidanceAgent(train_env, state_size, action_size)
# Load existing policy
if train_params.load_policy is not None:
@@ -244,6 +243,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params, clo
training_id
))
+ rl_policy = policy
for episode_idx in range(n_episodes + 1):
step_timer = Timer()
reset_timer = Timer()
@@ -254,6 +254,18 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params, clo
# Reset environment
reset_timer.start()
obs, info = train_env.reset(regenerate_rail=True, regenerate_schedule=True)
+
+ # train different number of agents : 1,2,3,... n_agents
+ for handle in range(train_env.get_num_agents()):
+ if (episode_idx % n_agents) < handle:
+ train_env.agents[handle].status = RailAgentStatus.DONE_REMOVED
+
+ # start with simple deadlock avoidance agent policy (imitation learning?)
+ if episode_idx < 500:
+ policy = DeadLockAvoidanceAgent(train_env, state_size, action_size)
+ else:
+ policy = rl_policy
+
policy.reset()
reset_timer.end()
@@ -512,7 +524,7 @@ if __name__ == "__main__":
parser.add_argument("--eps_start", help="max exploration", default=0.5, type=float)
parser.add_argument("--eps_end", help="min exploration", default=0.01, type=float)
parser.add_argument("--eps_decay", help="exploration decay", default=0.9985, type=float)
- parser.add_argument("--buffer_size", help="replay buffer size", default=int(1e5), type=int)
+ parser.add_argument("--buffer_size", help="replay buffer size", default=int(1e6), type=int)
parser.add_argument("--buffer_min_size", help="min buffer size to start training", default=0, type=int)
parser.add_argument("--restore_replay_buffer", help="replay buffer to restore", default="", type=str)
parser.add_argument("--save_replay_buffer", help="save replay buffer at each evaluation interval", default=False,
@@ -528,8 +540,8 @@ if __name__ == "__main__":
parser.add_argument("--load_policy", help="policy filename (reference) to load", default="", type=str)
parser.add_argument("--use_extra_observation", help="extra observation", default=True, type=bool)
parser.add_argument("--close_following", help="enable close following feature", default=True, type=bool)
- parser.add_argument("--max_depth", help="max depth", default=1, type=int)
- parser.add_argument("-t", "--training_env_config", help="training config id (eg 0 for Test_0)", default=1,
+ parser.add_argument("--max_depth", help="max depth", default=2, type=int)
+ parser.add_argument("-t", "--training_env_config", help="training config id (eg 0 for Test_0)", default=2,
type=int)
parser.add_argument("--render", help="render 1 episode in 100", default=False, type=bool)
diff --git a/utils/dead_lock_avoidance_agent.py b/utils/dead_lock_avoidance_agent.py
index 62e76ea09bcaf4a58ab982c89399d2e9fe889f2c..39090a14edbe533297556853f8aecc8b34ddef14 100644
--- a/utils/dead_lock_avoidance_agent.py
+++ b/utils/dead_lock_avoidance_agent.py
@@ -148,6 +148,6 @@ class DeadLockAvoidanceAgent(Policy):
for opp_a in opp_agents:
opp = full_shortest_distance_agent_map[opp_a]
delta = ((delta - opp - agent_positions_map) > 0).astype(int)
- if (np.sum(delta) < 1 + len(opp_agents)):
+ if (np.sum(delta) < 2 + len(opp_agents)):
next_step_ok = False
return next_step_ok
diff --git a/utils/extra.py b/utils/extra.py
index 340cb404a50bf9e19387e4ca227345a39d0f9db4..89ed0bb9ea2b7a993fe9eecd9332f0910294cbce 100644
--- a/utils/extra.py
+++ b/utils/extra.py
@@ -183,14 +183,21 @@ class Extra(ObservationBuilder):
self.build_data()
return
- def fast_argmax(self, array):
- if array[0] == 1:
- return 0
- if array[1] == 1:
- return 1
- if array[2] == 1:
- return 2
- return 3
+
+ def _check_dead_lock_at_branching_position(self, handle, new_position, branch_direction):
+ _, full_shortest_distance_agent_map = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
+ opp_agents = self.dead_lock_avoidance_agent.shortest_distance_walker.opp_agent_map.get(handle, [])
+ local_walker = DeadlockAvoidanceShortestDistanceWalker(
+ self.env,
+ self.dead_lock_avoidance_agent.shortest_distance_walker.agent_positions,
+ self.dead_lock_avoidance_agent.shortest_distance_walker.switches)
+ local_walker.walk_to_target(handle, new_position, branch_direction)
+ shortest_distance_agent_map, _ = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
+ my_shortest_path_to_check = shortest_distance_agent_map[handle]
+ next_step_ok = self.dead_lock_avoidance_agent.check_agent_can_move(my_shortest_path_to_check,
+ opp_agents,
+ full_shortest_distance_agent_map)
+ return next_step_ok
def _explore(self, handle, new_position, new_direction, depth=0):
@@ -332,18 +339,7 @@ class Extra(ObservationBuilder):
observation[18 + dir_loop] = has_same_agent
observation[22 + dir_loop] = has_switch
- _, full_shortest_distance_agent_map = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
- opp_agents = self.dead_lock_avoidance_agent.shortest_distance_walker.opp_agent_map.get(handle, [])
- local_walker = DeadlockAvoidanceShortestDistanceWalker(
- self.env,
- self.dead_lock_avoidance_agent.shortest_distance_walker.agent_positions,
- self.dead_lock_avoidance_agent.shortest_distance_walker.switches)
- local_walker.walk_to_target(handle, new_position, branch_direction)
- shortest_distance_agent_map, _ = self.dead_lock_avoidance_agent.shortest_distance_walker.getData()
- my_shortest_path_to_check = shortest_distance_agent_map[handle]
- next_step_ok = self.dead_lock_avoidance_agent.check_agent_can_move(my_shortest_path_to_check,
- opp_agents,
- full_shortest_distance_agent_map)
+ next_step_ok = self._check_dead_lock_at_branching_position(handle, new_position, branch_direction)
if next_step_ok:
observation[26 + dir_loop] = 1