ready for version 0.4.1

CHANGELOG.rst

@@ -4,6 +4,17 @@ Change Log
 --------
 - stack multiple states in `utils/DeepQAgent`
 
+[0.4.1] - 2020-06-16
+-----------------------
+- [FIXED] `Issue 14 <https://github.com/rte-france/l2rpn-baselines/issues/14>`_ clearer interface and get rid
+  of the "nb_env" in some baselines constructor. A helper function
+  `make_multi_env` has also been created to help the creation of the appropariate multi environment.
+- [FIXED] `Issue 13 <https://github.com/rte-france/l2rpn-baselines/issues/13>`_ the name have been properly updated
+- [FIXED] `Issue 12 <https://github.com/rte-france/l2rpn-baselines/issues/12>`_ the appropriate documentation for the
+  SAC baselines and all the kind
+- [FIXED] `Issue 9 <https://github.com/rte-france/l2rpn-baselines/issues/9>`_ no more hard coded global variables for
+  most of the baselines.
+
 [0.4.0] - 2020-06-xx
 --------------------
 - [ADDED] convenience way to modify the architecture of the neural networks

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'Benjamin DONNOT'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4.0'
+release = '0.4.1'
 version = '0.4'
 
 # -- General configuration ---------------------------------------------------

l2rpn_baselines/SAC/SAC_NN.py

@@ -39,12 +39,14 @@ class SAC_NN(BaseDeepQ):
     """
     def __init__(self,
                  nn_params,
-                 training_param=None):
+                 training_param=None,
+                 verbose=False):
         if training_param is None:
             training_param = TrainingParam()
         BaseDeepQ.__init__(self,
                            nn_params,
-                           training_param)
+                           training_param,
+                           verbose=verbose)
 
         # TODO add as meta param the number of "Q" you want to use (here 2)
         # TODO add as meta param size and types of the networks
@@ -248,7 +250,8 @@ def load_network(self, path, name=None, ext="h5"):
         self.model_Q = load_model('{}.{}'.format(path_modelQ, ext))
         self.model_Q2 = load_model('{}.{}'.format(path_modelQ2, ext))
         self.model_policy = load_model('{}.{}'.format(path_policy, ext))
-        print("Succesfully loaded network.")
+        if self.verbose:
+            print("Succesfully loaded network.")
 
     def target_train(self):
         """

l2rpn_baselines/utils/__init__.py

@@ -5,6 +5,7 @@
 # you can obtain one at http://mozilla.org/MPL/2.0/.
 # SPDX-License-Identifier: MPL-2.0
 # This file is part of L2RPN Baselines, L2RPN Baselines a repository to host baselines for l2rpn competitions.
+__version__ = "0.4.1"
 
 __all__ = [
     "cli_eval",
@@ -33,4 +34,4 @@
 from l2rpn_baselines.utils.NNParam import NNParam
 from l2rpn_baselines.utils.ReplayBuffer import ReplayBuffer
 from l2rpn_baselines.utils.BaseDeepQ import BaseDeepQ
-from l2rpn_baselines.utils.DeepQAgent import DeepQAgent
+from l2rpn_baselines.utils.DeepQAgent import DeepQAgent

setup.py

@@ -8,12 +8,12 @@
 
 import setuptools
 from setuptools import setup
-__version__ = "0.4.0"
+__version__ = "0.4.1"
 
 
 pkgs = {
     "required": [
-        "grid2op[challenge,optional]>=0.9.1.post1"
+        "grid2op[optional]>=0.9.1.post1"
     ],
     "extras": {
         "docs": [
@@ -22,7 +22,8 @@
             "sphinx-rtd-theme>=0.4.3",
             "sphinxcontrib-trio>=1.1.0",
             "autodocsumm>=0.1.13"
-        ]
+        ],
+        "challenge": ["grid2op[challenge]>=0.9.1.post1"]
     }
 }