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updated links in README and notebook

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Peter Zhokhov
2018-11-07 16:23:32 -08:00
parent 9705773eab 7bb405c7a7
commit 6b41b6b984
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README.md
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@@ -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 *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 ## 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 ## Subpackages

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docs/viz/viz.ipynb
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@@ -7,7 +7,7 @@
"id": "Ynb-laSwmpac" "id": "Ynb-laSwmpac"
}, },
"source": [ "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." "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."
] ]
}, },