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diff --git a/README.md b/README.md
@@ -1,5 +1,5 @@
 
-# Practical_RL [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/spring19)
+# Practical_RL [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/master)
 An open course on reinforcement learning in the wild.
 Taught on-campus at [HSE](https://cs.hse.ru) and [YSDA](https://yandexdataschool.com/)  and maintained to be friendly to online students (both english and russian).
 
@@ -22,7 +22,7 @@ Taught on-campus at [HSE](https://cs.hse.ru) and [YSDA](https://yandexdataschool
 * Virtual course environment: 
     * [Installing dependencies](https://github.com/yandexdataschool/Practical_RL/issues/1) on your local machine (recommended).
     * [__google colab__](https://colab.research.google.com/) - set open -> github -> yandexdataschool/pracical_rl -> {branch name} and select any notebook you want.
-    * Alternatives: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/spring19) and [Azure Notebooks](https://notebooks.azure.com/).
+    * Alternatives: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/yandexdataschool/practical_rl/master) and [Azure Notebooks](https://notebooks.azure.com/).
 
 
 # Additional materials

diff --git a/week01_intro/README.md b/week01_intro/README.md
@@ -23,7 +23,7 @@
 
 
 ## Practice assignment
-Instant dive in: [__seminar_gym_interface__](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week01_intro/seminar_gym_interface.ipynb), [__crossentropy_method__](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week01_intro/crossentropy_method.ipynb)
+Instant dive in: [__seminar_gym_interface__](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week01_intro/seminar_gym_interface.ipynb), [__crossentropy_method__](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week01_intro/crossentropy_method.ipynb)
 
 * Open `gym_interface.ipynb` and follow instructions from there
 * After you're done there, proceed to `crossentropy_method.ipynb`

diff --git a/week02_value_based/README.md b/week02_value_based/README.md
@@ -9,6 +9,6 @@
 
 ## Homework description:
 
-The main assignment is `seminar_vi.ipynb`[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week02_value_based/seminar_vi.ipynb) notebook in this week's folder. It has no requirements besides the most basic data science libraries (e.g. numpy) so you should be able to run it locally.
+The main assignment is `seminar_vi.ipynb`[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week02_value_based/seminar_vi.ipynb) notebook in this week's folder. It has no requirements besides the most basic data science libraries (e.g. numpy) so you should be able to run it locally.
 
 __Note:__ if you have any difficulty using graphviz, just set `has_graphviz=False`.
diff --git a/week02_value_based/seminar_vi.ipynb b/week02_value_based/seminar_vi.ipynb
@@ -34,7 +34,7 @@
    "outputs": [],
    "source": [
     "# If you Colab, uncomment this please\n",
-    "# !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/spring19/week02_value_based/mdp.py\n",
+    "# !wget -q https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week02_value_based/mdp.py\n",
     "\n",
     "transition_probs = {\n",
     "    's0': {\n",

diff --git a/week03_model_free/README.md b/week03_model_free/README.md
@@ -19,12 +19,12 @@
 
 Just as usual, start with 
 - `seminar_qlearning.ipynb` 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week03_model_free/seminar_qlearning.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week03_model_free/seminar_qlearning.ipynb)
 
 and then proceed to 
 
 - `homework.ipynb`
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week03_model_free/homework.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week03_model_free/homework.ipynb)
 
 Please pay attention for uncommenting first lines in code if you use Colab.
 
@@ -49,7 +49,7 @@ python pacman.py -p PacmanQAgent -x 5000 -n 5010 -l smallGrid # example
 * Make sure you can tune agent to beat ./run_crawler.sh
  * on windows, just run `python crawler.py` from cmd in the project directory
 * other ./run* files are mostly for your amusement. 
-  * ./run_pacman.sh will need more epochs to converge, see [comments](https://github.com/yandexdataschool/Practical_RL/blob/spring19/week03_model_free/seminar_py2/run_pacman.sh)
+  * ./run_pacman.sh will need more epochs to converge, see [comments](https://github.com/yandexdataschool/Practical_RL/blob/master/week03_model_free/seminar_py2/run_pacman.sh)
   * on windows, just copy the type `python pacman.py -p PacmanQAgent -x 2000 -n 2010 -l smallGrid` in cmd from assignemnt dir
 (YSDA/HSE) Please submit only qlearningAgents.py file and include a brief text report as comments in it.
 
diff --git a/week04_[recap]_deep_learning/README.md b/week04_[recap]_deep_learning/README.md
@@ -30,7 +30,7 @@ __Note:__ This week's materials cover the basics of neural nets and deep learnin
 
 
 ### Practice
-__[Colab url (pytorch)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week04_%5Brecap%5D_deep_learning/seminar_pytorch.ipynb)__
+__[Colab url (pytorch)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week04_%5Brecap%5D_deep_learning/seminar_pytorch.ipynb)__
 From now on, we'll have two tracks: theano and tensorflow. We'll also add pytorch seminars as soon as they're ready.
 
 Please pick seminar_theano.ipynb, seminar_tensorflow.ipynb or seminar_pytorch.ipynb.

diff --git a/week04_approx_rl/README.md b/week04_approx_rl/README.md
@@ -34,9 +34,9 @@
 
 ## Practice
 
-* Seminar: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week04_approx_rl/seminar_pytorch.ipynb)
-* Homework (main): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week04_approx_rl/homework_pytorch_main.ipynb#scrollTo=KVvvo7k_ap8w)
-* Homework (debug): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week04_approx_rl/homework_pytorch_debug.ipynb#scrollTo=KVvvo7k_ap8w)
+* Seminar: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week04_approx_rl/seminar_pytorch.ipynb)
+* Homework (main): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week04_approx_rl/homework_pytorch_main.ipynb#scrollTo=KVvvo7k_ap8w)
+* Homework (debug): [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week04_approx_rl/homework_pytorch_debug.ipynb#scrollTo=KVvvo7k_ap8w)
 
 
 

diff --git a/week04_approx_rl/homework_pytorch_debug.ipynb b/week04_approx_rl/homework_pytorch_debug.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "# os.system('python -m pip install -U pygame --user')\n",
     "\n",
-    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/spring19/week04_approx_rl/'\n",
+    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week04_approx_rl/'\n",
     "\n",
     "# os.system('wget ' + prefix + 'atari_wrappers.py')\n",
     "# os.system('wget ' + prefix + 'utils.py')\n",

diff --git a/week04_approx_rl/homework_pytorch_main.ipynb b/week04_approx_rl/homework_pytorch_main.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "# os.system('python -m pip install -U pygame --user')\n",
     "\n",
-    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/spring19/week04_approx_rl/'\n",
+    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week04_approx_rl/'\n",
     "\n",
     "# os.system('wget ' + prefix + 'atari_wrappers.py')\n",
     "# os.system('wget ' + prefix + 'utils.py')\n",

diff --git a/week04_approx_rl/seminar_pytorch.ipynb b/week04_approx_rl/seminar_pytorch.ipynb
@@ -77,7 +77,7 @@
     "\n",
     "Since we're working with a pre-extracted features (cart positions, angles and velocities), we don't need a complicated network yet. In fact, let's build something like this for starters:\n",
     "\n",
-    "![img](https://raw.githubusercontent.com/yandexdataschool/Practical_RL/spring19/yet_another_week/_resource/qlearning_scheme.png)\n",
+    "![img](https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/yet_another_week/_resource/qlearning_scheme.png)\n",
     "\n",
     "For your first run, please only use linear layers (nn.Linear) and activations. Stuff like batch normalization or dropout may ruin everything if used haphazardly. \n",
     "\n",

diff --git a/week05_explore/README.md b/week05_explore/README.md
@@ -1,4 +1,4 @@
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week05_explore/week5.ipynb) 
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week05_explore/week5.ipynb) 
 
 ### Slides - [here](https://yadi.sk/i/H0zVBROe3TWWHz)
 

diff --git a/week06_policy_based/README.md b/week06_policy_based/README.md
@@ -19,6 +19,6 @@
 * Adversarial review of policy gradient - [blog](http://www.argmin.net/2018/02/20/reinforce/)
 
 
-Run seminar notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week06_policy_based/reinforce_pytorch.ipynb)
+Run seminar notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week06_policy_based/reinforce_pytorch.ipynb)
 
-Run optional homework notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week06_policy_based/a2c-optional.ipynb)
+Run optional homework notebook in colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week06_policy_based/a2c-optional.ipynb)
diff --git a/week07_[recap]_rnn/README.md b/week07_[recap]_rnn/README.md
@@ -13,7 +13,7 @@
 * OpenAI research on sentiment analysis that sheds some light on what's inside LSTM language model.
 
 # Homework description
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week07_%5Brecap%5D_rnn/seminar_pytorch.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week07_%5Brecap%5D_rnn/seminar_pytorch.ipynb)
 
 This week's practice gets you acquainted with basics of recurrent neural networks. For simplicity, we'll train them on character language modelling task. Pick any one of `seminar_lasagne`, `seminar_lasagne_ingraph` or `seminar_tf`.
 

diff --git a/week07_[recap]_rnn/seminar_pytorch.ipynb b/week07_[recap]_rnn/seminar_pytorch.ipynb
@@ -242,7 +242,7 @@
         "# Recurrent neural network\n",
         "\n",
         "We can rewrite recurrent neural network as a consecutive application of dense layer to input $x_t$ and previous rnn state $h_t$. This is exactly what we're gonna do now.\n",
-        "<img src=\"https://github.com/yandexdataschool/Practical_RL/blob/spring19/week07_%5Brecap%5D_rnn/rnn.png?raw=1\" width=480>\n",
+        "<img src=\"https://github.com/yandexdataschool/Practical_RL/blob/master/week07_%5Brecap%5D_rnn/rnn.png?raw=1\" width=480>\n",
         "\n",
         "Since we're training a language model, there should also be:\n",
         "* An embedding layer that converts character id x_t to a vector.\n",

diff --git a/week07_seq2seq/README.md b/week07_seq2seq/README.md
@@ -10,7 +10,7 @@
 
 ## Practice
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week07_seq2seq/practice_torch.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week07_seq2seq/practice_torch.ipynb)
 
 
 As usual, go to practice_{your framework}.ipynb above and follow instructions from there. [pytorch](./practice_torch.ipynb), [tensorflow](./practice_tf.ipynb), [theano](./practice_theano.ipynb)

diff --git a/week08_pomdp/README.md b/week08_pomdp/README.md
@@ -23,6 +23,6 @@ _Links on all articles mentioned during the lecture could be found in "Reference
 
 # Practice
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week08_pomdp/practice_pytorch.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week08_pomdp/practice_pytorch.ipynb)
 
 The assignment is platform and framewerk independent, so choose the framework that suits you best, but pay attention on how many you will need to implement youself in case of nonstandart ones.
diff --git a/week09_policy_II/README.md b/week09_policy_II/README.md
@@ -10,9 +10,9 @@ This section covers some steroids for policy gradient methods, along with a cool
 * Original articles - [TRPO](https://arxiv.org/abs/1502.05477), [NPG](https://papers.nips.cc/paper/2073-a-natural-policy-gradient.pdf)
 
 ## Practice
-* Seminar: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week09_policy_II/seminar_TRPO_pytorch.ipynb)
+* Seminar: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week09_policy_II/seminar_TRPO_pytorch.ipynb)
 
-* Homework: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week09_policy_II/ppo.ipynb)
+* Homework: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week09_policy_II/ppo.ipynb)
 
 ## More: Reinforcement learning in large/continuous action spaces
 While you already know algorithms that will work with continuously many actions, it can't hurt to learn something more specialized.

diff --git a/week09_policy_II/ppo.ipynb b/week09_policy_II/ppo.ipynb
@@ -16,7 +16,7 @@
     "# os.system('pip install pyglet==1.2.4')\n",
     "# os.system('pip install gym')\n",
     "\n",
-    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/spring19/week06_policy_II/'\n",
+    "# prefix = 'https://raw.githubusercontent.com/yandexdataschool/Practical_RL/master/week06_policy_II/'\n",
     "\n",
     "# os.system('wget ' + prefix + 'runners.py')\n",
     "# os.system('wget ' + prefix + 'mujoco_wrappers.py')\n",

diff --git a/week09_policy_II/seminar_TRPO_pytorch.ipynb b/week09_policy_II/seminar_TRPO_pytorch.ipynb
@@ -765,7 +765,7 @@
     "\n",
     "![img](https://s17.postimg.org/i90chxgvj/vine.png)\n",
     "\n",
-    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/spring19/week10_planning/seminar_MCTS.ipynb).\n",
+    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/master/week10_planning/seminar_MCTS.ipynb).\n",
     "\n",
     "You can read more about in the [TRPO article](https://arxiv.org/abs/1502.05477) in section 5.2.\n",
     "\n",

diff --git a/week09_policy_II/seminar_TRPO_tensorflow.ipynb b/week09_policy_II/seminar_TRPO_tensorflow.ipynb
@@ -718,7 +718,7 @@
     "\n",
     "![img](https://s17.postimg.org/i90chxgvj/vine.png)\n",
     "\n",
-    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/spring19/week10_planning/seminar_MCTS.ipynb).\n",
+    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/master/week10_planning/seminar_MCTS.ipynb).\n",
     "\n",
     "You can read more about in the [TRPO article](https://arxiv.org/abs/1502.05477) in section 5.2.\n",
     "\n",

diff --git a/week09_policy_II/seminar_TRPO_theano.ipynb b/week09_policy_II/seminar_TRPO_theano.ipynb
@@ -718,7 +718,7 @@
     "\n",
     "![img](https://s17.postimg.org/i90chxgvj/vine.png)\n",
     "\n",
-    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/spring19/week10_planning/seminar_MCTS.ipynb).\n",
+    "In most gym environments, you can actually backtrack by using states. You can find a wrapper that saves/loads states in [the mcts seminar](https://github.com/yandexdataschool/Practical_RL/blob/master/week10_planning/seminar_MCTS.ipynb).\n",
     "\n",
     "You can read more about in the [TRPO article](https://arxiv.org/abs/1502.05477) in section 5.2.\n",
     "\n",

diff --git a/week10_planning/README.md b/week10_planning/README.md
@@ -1,7 +1,7 @@
 ## Assignments
 
 Just as usual, start with  `seminar_MCTS.ipynb` 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/spring19/week10_planning/seminar_MCTS.ipynb)
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/yandexdataschool/Practical_RL/blob/master/week10_planning/seminar_MCTS.ipynb)
 
 ## Materials: planning