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added a note in README about TfRunningMeanStd and serialization of VecNormalize

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Peter Zhokhov
2018-08-08 10:44:58 -07:00
parent 5a7f9847d8
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README.md
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@@ -100,15 +100,17 @@ DQN with Atari is at this point a classics of benchmarks. To run the baselines i
## Saving, loading and visualizing models ## Saving, loading and visualizing models
The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models. The algorithms serialization API is not properly unified yet; however, there is a simple method to save / restore trained models.
`--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively. `--save_path` and `--load_path` command-line option loads the tensorflow state from a given path before training, and saves it after the training, respectively.
Let's imagine you'd like to train ppo2 on MuJoCo humanoid, save the model and then later visualize what has it learnt. Let's imagine you'd like to train ppo2 on Atari Pong, save the model and then later visualize what has it learnt.
```bash ```bash
python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --num-timesteps=2e7 --save_path=~/models/humanoid_20M_ppo2 python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num-timesteps=2e7 --save_path=~/models/pong_20M_ppo2
``` ```
This should get to the mean reward per episode about 5k. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize: This should get to the mean reward per episode about 5k. To load and visualize the model, we'll do the following - load the model, train it for 0 steps, and then visualize:
```bash ```bash
python -m baselines.run --alg=ppo2 --env=Humanoid-v2 --num-timesteps=0 --load_path=~/models/humanoid_20M_ppo2 --play python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num-timesteps=0 --load_path=~/models/pong_20M_ppo2 --play
``` ```
*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