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View Code? Open in Web Editor NEWOffline Risk-Averse Actor-Critic (O-RAAC). A model-free RL algorithm for risk-averse RL in a fully offline setting
License: MIT License
Offline Risk-Averse Actor-Critic (O-RAAC). A model-free RL algorithm for risk-averse RL in a fully offline setting
License: MIT License
Hi,
Thanks for providing this interesting package.
We are very interesting in your paper Risk-Averse Offline Reinforcement Learning and would like to replicate the results therein. However, I encounter two problem in trying to replicate the results.
In the two of the json files at O-RAAC/json_params/ORAAC/
(Params_cheetah_expert.json
and Params_cheetah_medium.json
) you have "RISK_DISTORTION": "wang"
, on other json files you have "RISK_DISTORTION": "cvar"
. However, in your paper you use CVaR_{0.1} as the risk measure. May I ask whether I should replace the two "RISK_DISTORTION": "wang"
with "RISK_DISTORTION": "cvar"
as in other four json files? Are there any other parameters in the json files that we need to change in order to replicate Table 2 in your paper ?
Does this package support training the model with GPU? I do see a line of code parser.add_argument('--device', action="store", type=str)
in O-RAAC/utils/utilities.py/
but this argument seems used nowhere. I try to run you code in a GPU server using the command python3.8 oraac.py --device "cuda:0" --config_name 'Params_cheetah_medium.json'
and the GPU seems NOT used. (I move the oraac.py file out from the experiments
folder). Could you please let me know how did you train the models using GPUs.
Dear authors,
In you paper, there are customised reward functions for Half-Cheetah, Walker2D and Hopper. How can I find it in this repo?
Thank you!
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