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Nommie00 avatar Nommie00 commented on June 27, 2024 1

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.

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pseudo-rnd-thoughts avatar pseudo-rnd-thoughts commented on June 27, 2024 1

The reward threshold is not used particular by any training algorithm so I would ignore it

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pseudo-rnd-thoughts avatar pseudo-rnd-thoughts commented on June 27, 2024

@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

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alejopaullier96 avatar alejopaullier96 commented on June 27, 2024

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

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