From a018012d862e7cc7d895237b6c0f80477a38b55d Mon Sep 17 00:00:00 2001
From: Guillaume Mollard <guillaume.mollard2@gmail.com>
Date: Thu, 13 Jun 2019 22:35:02 +0200
Subject: [PATCH] new metric
---
RLLib_training/RailEnvRLLibWrapper.py | 7 +++++--
RLLib_training/train_experiment.py | 15 +++++++++++++++
2 files changed, 20 insertions(+), 2 deletions(-)
diff --git a/RLLib_training/RailEnvRLLibWrapper.py b/RLLib_training/RailEnvRLLibWrapper.py
index 68a76e6..1dbdc28 100644
--- a/RLLib_training/RailEnvRLLibWrapper.py
+++ b/RLLib_training/RailEnvRLLibWrapper.py
@@ -6,6 +6,8 @@ from flatland.envs.generators import complex_rail_generator, random_rail_generat
import numpy as np
+
+
class RailEnvRLLibWrapper(MultiAgentEnv):
def __init__(self, config):
@@ -25,6 +27,7 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
self.rail_generator = random_rail_generator()
elif config['rail_generator'] == "load_env":
self.predefined_env = True
+ self.rail_generator = random_rail_generator()
else:
raise(ValueError, f'Unknown rail generator: {config["rail_generator"]}')
@@ -36,8 +39,8 @@ class RailEnvRLLibWrapper(MultiAgentEnv):
prediction_builder_object=config['predictor'])
if self.predefined_env:
- self.env.load(config['load_env_path'])
- # '/home/guillaume/EPFL/Master_Thesis/flatland/baselines/torch_training/railway/complex_scene.pkl')
+ #self.env.load(config['load_env_path'])
+ self.env.load('/home/guillaume/EPFL/Master_Thesis/flatland/baselines/torch_training/railway/complex_scene.pkl')
self.width = self.env.width
self.height = self.env.height
diff --git a/RLLib_training/train_experiment.py b/RLLib_training/train_experiment.py
index 4d3a2a9..90ce648 100644
--- a/RLLib_training/train_experiment.py
+++ b/RLLib_training/train_experiment.py
@@ -48,6 +48,21 @@ ModelCatalog.register_custom_model("conv_model", ConvModelGlobalObs)
ray.init()#object_store_memory=150000000000, redis_max_memory=30000000000)
+def on_episode_start(info):
+ episode = info['episode']
+ map_width = info['env'].envs[0].width
+ map_height = info['env'].envs[0].height
+ episode.horizon = map_width + map_height
+
+
+def on_episode_step(info):
+ episode = info['episode']
+
+
+def on_episode_end(info):
+ episode = info['episode']
+
+
def train(config, reporter):
print('Init Env')
--
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