Comments (4)
Simply add an argument 'max_episode_steps' when you are trying to initialize the environment,
e.g. env = gym.make("MountainCar-v0", max_episode_steps=2000, render_mode="human")
which can change the max_episode_steps
in the EnvSpec
.
While truncation is always False in Continuous_MountainCarEnv
, using gymnasium.wrappers.TimeLimit
may help you to add truncated
in steps
.
Hope this can answer your question.
from gymnasium.
The reward threshold is not used particular by any training algorithm so I would ignore it
from gymnasium.
@Nommie00 is correct, you can modify any of the EnvSpec
use gym.make(..., keyword=)
except the kwargs
which you can just specify normally.
I'm closing but respond if there is anything unclear or not working
from gymnasium.
Thanks Norman and Mark for the quick responses. The methods mentioned indeed work. Can you answer my second question in the post? Does the reward threshold need to be changed if the maximum episode steps change? I see that for the Continuous_MountainCarEnv
is set by default to +90.0
from gymnasium.
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