Accompanying code for "Deep Reinforcement Learning that Matters"
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baselines @ 50ffe01
modular_rl
rllab
rllabplusplus
tools Add plotting script for graphs in case people want to use it. Sep 22, 2017
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README.md

README.md

DeepReinforcementLearningThatMatters

Accompanying code for "Deep Reinforcement Learning that Matters"

Baselines Experiments

Our Fork

Current Baselines Code

Our checkpointed version of the baselines code is found in the baselines folder. We make several modifications, mostly to allow for passing network structures as arguments to the MuJoCo-related run scripts.

Our only change internally was to the DDPG evaluation code. We do this to allow for comparison against other algorithms. In the DDPG code, evaluation is done across N different policies where N is the number of "epoch_cycles", we did not find this to be consistent for comparison against other methods, so we modify this to match the rllab version of DDPG evaluation. That is, we run on the target policy for 10 full trajectories at the end of an epoch.

rllab experiments

rllab code

These require the full rllab code, which we do not provide. Instead we provide some run scripts for rllab experiments in the rllab folder.

rllabplusplus experiments

rllabplusplus (Q-Prop) code

This is the code provided with QPROP, we only provide a checkpointed version of the DDPG code which we use for evaluation here. This is under the rllabplusplus folder.

modular_rl experiments

Original TRPO (Modular RL) Code

These are simply run scripts for the modular rl codebase.

Tools

This contains tools for significance testing which we used. And various associated run scripts.

For bootstrap-based analysis, we use the bootstrapped repo. Tutorials there are a nice introduction to this sort of statistical analysis.

For t-test and KS test we use the scipy tools.

Citation

@article{hendersonRL2017,
   author = {{Henderson}, Peter and {Islam}, Riashat and {Bachman}, Philip and {Pineau}, Joelle and {Precup}, Doina and {Meger}, David},
    title = "{Deep Reinforcement Learning that Matters}",
  journal = {arXiv preprint arXiv:1709.06560},
     year = 2017,
       url={https://arxiv.org/pdf/1709.06560.pdf}
}