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EMU-Q

Exploring by Minimizing Uncertainty of Q values (EMU-Q) as presented in "Bayesian RL for Goal-Only Rewards" at CoRL'18 by P. Morere and F. Ramos [PDF].

If you use any of the code related to this repository in a paper, research etc., please cite:

@inproceedings{
    morere2018bayesian,
    title={Bayesian {RL} for Goal-Only Rewards},
    author={Morere, Philippe and Ramos, Fabio},
    booktitle={Conference on Robot Learning},
    year={2018},
}

Dependencies

This code is written for python3. The dependencies (pip packages) are:

  • numpy
  • scipy
  • gym
  • nlopt
  • ghalton
  • tqdm

Running the code

The code entry point is main.py. Try run python3 main.py --help for available options.

Running our method

python3 main.py --agent=method --sparseGymEnv=MountainCar-v0 --nStep=300 --nEp=10 --nRFF=300 --sigmaS=0.35 --sigmaA=10 -vv

Running RFF-Q

python3 main.py --agent=QLearning --gymEnv=MountainCar-v0 --nStep=300 --nEp=30 --nRFF=300 --sigmaS=0.35 --sigmaA=10 -vv

goal-only discrete and continuous gym environments

All goal-only discrete and continuous gym environments presented in the main paper are located in the gymEnvs folder. To use them, these environments need to be registered in gym as described in https://gym.openai.com/docs/#the-registry. These environments can then be called from main.py with --gymEnv=SparseMountainCar-v0 for example.