Update unity_pettingzoo_base_env.py#6136
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+1, ran into the same problem. This is especially problematic when only using the mlagents-envs package for environment interfacing, but using own training algorithms (e.g., StableBaselines3). |
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Migrate from Gym to Gymnasium (gymnasium>=0.26.0) Upgrade Pettingzoo to 1.24.0 Relax the dependencies for Numpy Reference PRs: - #6211 Pettingzoo wrapper changed to gymnasium interface - #6203 Upgrade to Gymnasium interface - #6166 Update Gym to Gymnasium - #6136 Update unity_pettingzoo_base_env.py - #6060 Upgraded from gym to Gymnasium Co-authored-by: alexander-zap <65029346+alexander-zap@users.noreply.github.com> Co-authored-by: AmineAndam04 <andamamine83@gmail.com> Co-authored-by: Olivia Bayley <150163193+OliviaBayley@users.noreply.github.com> Co-authored-by: Vadim Liventsev <dev@vadim.me> Co-authored-by: Zachary Laborde <zachlaborde93@gmail.com>
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in the the
_update_action_spacesmethod of the UnityPettingzooBaseEnv class, you force the actions to be of int32 and in the [-1,1] range (see the code below). This constraint doesn't apply to all scenariosIt makes more sense to use the same logic as in
_update_observation_spaces.We can generalize to all scenarios by using:
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