google-deepmind
Making Tasks Infinite Horizon
Hi, from the White Paper I found that these tasks terminate after 1000 steps. I'm working with Infinite Horizon Problems for which I need continuing/ infinite-horizon/ never ending tasks. Is there any way by which I can remove the 1000 steps constraint.
Also, I want to train a DQN agent for which I need discrete actions. Is there any way of discretezing the continuous action space for this suite?
Any help would be really appreciated. Thanks.
Regarding the first question, I believe that you there is an option to specify a time limit, see for example https://github.com/deepmind/dm_control/blob/4f1a9944bf74066b1ffe982632f20e6c687d45f1/dm_control/suite/cartpole.py#L62
To use this, I think something like
from dm_control import suite
suite.load(
"pendulum",
"swingup",
task_kwargs={"time_limit", float("inf"))},
)
would work.
Indeed, you can take a look at the LQR task, which has a terminal state condition rather than a time limit.
On Thu, 11 Nov 2021 at 23:13, Yicheng Luo @.***> wrote:
Regarding the first question, I believe that you there is an option to specify a time limit, see for example
https://github.com/deepmind/dm_control/blob/4f1a9944bf74066b1ffe982632f20e6c687d45f1/dm_control/suite/cartpole.py#L62
To use this, I think something like
from dm_control import suite suite.load( "pendulum", "swingup", task_kwargs={"time_limit", float("inf"))}, )
would work.
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Dr. Yuval Tassa | Research Scientist | Google DeepMind | @.***
