[paper]

Reinforcement learning (RL) experiments have notoriously high variance. We demonstrate that one can optimize directly for lower variance, without hurting average performance. Specifically, using a few simple methods, we decrease the variance by a factor ~3x (over 21 DMC tasks) for the competitive actor-critic agent DRQv2 without decreasing the average reward. This repo contains minimal modifications on top of the DRQv2 code base to reproduce our results.

Simply use the conda environment:

conda env create -f conda_env.yml
conda activate drqv2

1. To run without any tricks enabled, simply run:

python train.py task=cheetah_run

2. To use all all tricks, run:

python train.py task=cheetah_run  agent.pnorm_critic=True agent.pnorm_actor=True agent.asymmetric_clip=True agent.action_penalty=0.0001 agent.cpc_until=10000

Note that the number of frames where CPC is used and the parameter for the action penalty are explicitly set.

3. Our proposed methods can be independently toggled with

   Method	Flags
   pnorm for critic	agent.pnorm_critic=True
   pnorm for actor	agent.pnorm_actor
   assymetric clip	agent.asymmetric_clip=True
   action penalty	agent.action_penalty=0.0001
   contrastive learning	agent.cpc_until=10000

For citation, please use:

@article{bjorck2021high,
  title={Is High Variance Unavoidable in RL? A Case Study in Continuous Control},
  author={Bjorck, Johan and Gomes, Carla P and Weinberger, Kilian Q},
  journal={arXiv preprint arXiv:2110.11222},
  year={2021}
}

and for DRQv2:

@article{yarats2021drqv2,
  title={Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning},
  author={Denis Yarats and Rob Fergus and Alessandro Lazaric and Lerrel Pinto},
  journal={arXiv preprint arXiv:2107.09645},
  year={2021}
}

Our experiments are built on top of the open-sourced code for DRQv2.