Add a characteristic for solvers using action masks and make use of it in rollout #445
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- Use it in rollout to make them be aware of current action mask. - Add a `get_action_mask()` method to domains by default converting applicable actions space into a 0-1 numpy array, provided that the action space of each agent is an EnumerableSpace.
- inherits from Maskable - do not require anymore FullObservable from the domain to use action masking, as get_action_mask() can be called without the solver knowing about the current state (and since in rollout, the actual domain is now used) - decide whether using action masking directly in __init__() so that using_applicable_actions() can be overriden properly - use common functions for unwrap_obs and wrap_action in solver and wrapper environment to avoid code duplication - use domain.get_action_mask() to convert applicable actions into a mask (the method is more efficient as not calling get_applicable_actions() for each actions)
This is more memory sufficient for only 0-1's. And seems to be the standard for action mask at least for ray.rllib, as shown in `action_mask_key` documentation at https://docs.ray.io/en/latest/rllib/rllib-training.html
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neo-alex
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Dec 16, 2024
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retrieve_applicable_actions()method.get_action_mask()method to domains by default converting applicable actions space into a 0-1 numpy array, provided that the action space of each agent is an EnumerableSpace.RayRLlibsolver handles action masking:MaskableFullObservablefrom the domain to use actionmasking, as
get_action_mask()can be called without the solver knowing aboutthe current state (and since in rollout, the actual domain is now
used)
__init__()so thatusing_applicable_actions()can be overriden properlyunwrap_obsandwrap_actionin solver andwrapper environment to avoid code duplication
domain.get_action_mask()to convert applicable actions into a mask(the method is more efficient as not calling
get_applicable_actions()for each actions)