multi-actions environment

[gitlabspy]

Any tutorials on warpping a (multi-actions) custom gym-env for tianshou?

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[Trinkle23897]

First, I assume your environment's action format is a dict, e.g.,

{"a": [3, 4], "b": 5.0}

the next step is to inherit an existing policy class and overwrite its forward function, so that the return value is a Batch that contains the desired action format, e.g.,

    def forward(self, ...):
        ...
        return Batch(act=Batch(a=..., b=...), ...)

Feel free to modify the way that calculates the result of a and b, for example, you can directly return a and b in your network forward. And that's it.

Note: in forward function, action a and b are batch data, i.e., a has a shape of [bsz, 2] and b has a shape of [bsz].