From f4fca1d59e9df6ccf1b21752ce33419d89753642 Mon Sep 17 00:00:00 2001
From: "Egli Adrian (IT-SCI-API-PFI)" <adrian.egli@sbb.ch>
Date: Mon, 30 Nov 2020 15:11:27 +0100
Subject: [PATCH] clean up code - simplified
---
reinforcement_learning/multi_agent_training.py | 10 +++++-----
run.py | 9 +++++++--
2 files changed, 12 insertions(+), 7 deletions(-)
diff --git a/reinforcement_learning/multi_agent_training.py b/reinforcement_learning/multi_agent_training.py
index 06c35b2..6944eab 100755
--- a/reinforcement_learning/multi_agent_training.py
+++ b/reinforcement_learning/multi_agent_training.py
@@ -208,7 +208,7 @@ def train_agent(train_params, train_env_params, eval_env_params, obs_params):
# Double Dueling DQN policy
policy = DDDQNPolicy(state_size, action_size, train_params)
- if False:
+ if True:
policy = PPOAgent(state_size, action_size, n_agents)
# Load existing policy
if train_params.load_policy is not "":
@@ -546,10 +546,10 @@ def eval_policy(env, tree_observation, policy, train_params, obs_params):
if __name__ == "__main__":
parser = ArgumentParser()
- parser.add_argument("-n", "--n_episodes", help="number of episodes to run", default=2000, type=int)
- parser.add_argument("-t", "--training_env_config", help="training config id (eg 0 for Test_0)", default=1,
+ parser.add_argument("-n", "--n_episodes", help="number of episodes to run", default=10000, type=int)
+ parser.add_argument("-t", "--training_env_config", help="training config id (eg 0 for Test_0)", default=0,
type=int)
- parser.add_argument("-e", "--evaluation_env_config", help="evaluation config id (eg 0 for Test_0)", default=1,
+ parser.add_argument("-e", "--evaluation_env_config", help="evaluation config id (eg 0 for Test_0)", default=0,
type=int)
parser.add_argument("--n_evaluation_episodes", help="number of evaluation episodes", default=5, type=int)
parser.add_argument("--checkpoint_interval", help="checkpoint interval", default=100, type=int)
@@ -573,7 +573,7 @@ if __name__ == "__main__":
parser.add_argument("--load_policy", help="policy filename (reference) to load", default="", type=str)
parser.add_argument("--use_fast_tree_observation", help="use FastTreeObs instead of stock TreeObs",
action='store_true')
- parser.add_argument("--max_depth", help="max depth", default=1, type=int)
+ parser.add_argument("--max_depth", help="max depth", default=2, type=int)
training_params = parser.parse_args()
env_params = [
diff --git a/run.py b/run.py
index 33bc56c..a5e91a3 100644
--- a/run.py
+++ b/run.py
@@ -30,6 +30,7 @@ from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.evaluators.client import FlatlandRemoteClient
from flatland.evaluators.client import TimeoutException
+from reinforcement_learning.ppo.ppo_agent import PPOAgent
from utils.dead_lock_avoidance_agent import DeadLockAvoidanceAgent
from utils.deadlock_check import check_if_all_blocked
from utils.fast_tree_obs import FastTreeObs
@@ -46,12 +47,14 @@ from reinforcement_learning.dddqn_policy import DDDQNPolicy
# Print per-step logs
VERBOSE = True
USE_FAST_TREEOBS = True
+USE_PPO_AGENT = True
# Checkpoint to use (remember to push it!)
checkpoint = "./checkpoints/201124171810-7800.pth" # 18.249244799876152 DEPTH=2 AGENTS=10
# checkpoint = "./checkpoints/201126150143-5200.pth" # 18.249244799876152 DEPTH=2 AGENTS=10
# checkpoint = "./checkpoints/201126160144-2000.pth" # 18.249244799876152 DEPTH=2 AGENTS=10
checkpoint = "./checkpoints/201127160352-2000.pth"
+checkpoint = "./checkpoints/201130083154-2000.pth"
EPSILON = 0.005
@@ -99,8 +102,10 @@ else:
action_size = 5
# Creates the policy. No GPU on evaluation server.
-policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
-# policy = PPOAgent(state_size, action_size, 10)
+if not USE_PPO_AGENT:
+ policy = DDDQNPolicy(state_size, action_size, Namespace(**{'use_gpu': False}), evaluation_mode=True)
+else:
+ policy = PPOAgent(state_size, action_size, 10)
policy.load(checkpoint)
#####################################################################
--
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