updated links in README and notebook

README.md

@@ -110,7 +110,7 @@ python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --
 *NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default
 
 ## Loading and vizualizing learning curves and other training metrics
-See [here](docs/viz/viz.md) for instructions on how to load and display the training data. 
+See [here](docs/viz/viz.ipynb) for instructions on how to load and display the training data. 
 
 ## Subpackages
 

docs/viz/viz.ipynb

@@ -7,7 +7,7 @@
     "id": "Ynb-laSwmpac"
    },
    "source": [
-    "# Loading and visualizing results\n",
+    "# Loading and visualizing results ([open in colab](https://colab.research.google.com/github/openai/baselines/blob/master/docs/viz.ipynb))\n",
     "In order to compare performance of algorithms, we often would like to visualize learning curves (reward as a function of time steps), or some other auxiliary information about learning aggregated into a plot. Baselines repo provides tools for doing so in several different ways, depending on the goal."
    ]
   },