From e2cede22a3dec5241a835c24184cb707d20cb1ba Mon Sep 17 00:00:00 2001
From: Erik Nygren <baerenjesus@gmail.com>
Date: Tue, 9 Jun 2020 09:35:27 +0000
Subject: [PATCH] Update multi_agent_training.py
---
torch_training/multi_agent_training.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index 0b5ccc7..68b0c40 100644
--- a/torch_training/multi_agent_training.py
+++ b/torch_training/multi_agent_training.py
@@ -44,10 +44,11 @@ def main(argv):
# Use a the malfunction generator to break agents from time to time
- stochastic_data = {'malfunction_rate': 8000, # Rate of malfunction occurence of single agent
- 'min_duration': 15, # Minimal duration of malfunction
- 'max_duration': 50 # Max duration of malfunction
- }
+ stochastic_data = MalfunctionParameters(malfunction_rate=10000, # Rate of malfunction occurence
+ min_duration=15, # Minimal duration of malfunction
+ max_duration=50 # Max duration of malfunction
+ )
+
# Custom observation builder
TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv(30))
@@ -71,6 +72,8 @@ def main(argv):
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
obs_builder_object=TreeObservation)
+ # Reset env
+ env.reset(True,True)
# After training we want to render the results so we also load a renderer
env_renderer = RenderTool(env, gl="PILSVG", )
# Given the depth of the tree observation and the number of features per node we get the following state_size
--
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