This model was trained from 3 trainings of PPO agents using different initial seeds. These agents were trained by checking out 983cb75. The best and last models were kept from each training. This submission has loaded the best models from each training, reevaluates them, and selects the best model from these latest evaluations (mean - std).
Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
By default training goes to a rl-algo-impls project while benchmarks go to
rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
models and the model weights are uploaded to WandB.
Before doing anything below, you'll need to create a wandb account and run wandb login.
Note: While the model state dictionary and hyperaparameters are saved, the latest
implementation could be sufficiently different to not be able to reproduce similar
results. You might need to checkout the commit the agent was trained on:
983cb75.
# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
python enjoy.py --wandb-run-path=sgoodfriend/rl-algo-impls-benchmarks/ugp4onfb
Setup hasn't been completely worked out yet, so you might be best served by using Google
Colab starting from the
colab_enjoy.ipynb
notebook.
Training
If you want the highest chance to reproduce these results, you'll want to checkout the
commit the agent was trained on: 983cb75. While
training is deterministic, different hardware will give different results.
As an alternative,
colab_benchmark.ipynb,
can be used. However, this requires a Google Colab Pro+ subscription and running across
4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
Hyperparameters
This isn't exactly the format of hyperparams in hyperparams/ppo.yml, but instead the Wandb Run Config. However, it's very
close and has some additional data:
