Module moog.action_spaces.composite
Composite action space that composes multiple action spaces.
This is often used for multi-agent games and for games where the subject gives multiple sources of control, e.g. eye position and joystick movement.
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"""Composite action space that composes multiple action spaces.
This is often used for multi-agent games and for games where the subject gives
multiple sources of control, e.g. eye position and joystick movement.
"""
from . import abstract_action_space
class Composite(abstract_action_space.AbstractActionSpace):
"""Composite action space.
Example usage:
joystick_action_space = maze_action_space.Joystick(
scaling_factor=0.1, action_layers='agent')
eye_action_space = action_spaces.SetPosition(
action_layers='eye_sprite')
action_space = action_spaces.Composite(
joystick=joystick_action_space,
eye=eye_action_space,
)
# Playing the game...
action = {'joystick': [0.1, -0.2], 'eye': [0.4, 0.8]}
env.step(action=action) # goes to action_space.step(state, action)
"""
def __init__(self, **action_spaces):
"""Constructor.
Args:
action_spaces: Dict. Keys are strings and values are action spaces.
An action is a dictionary with the same set of keys.
"""
self.action_spaces = action_spaces
self._action_keys = action_spaces.keys()
self._action_spec = {
key: value.action_spec() for key,value in self.action_spaces.items()
}
def step(self, state, action):
"""Apply action to environment state.
Args:
state: Ordereddict of layers of sprites. Environment state.
action: Dict. Keys much be the same as self._action_keys. Each value
will be fed into the action space of the corresponding key.
"""
for k, v in action.items():
self.action_spaces[k].step(state, v)
def reset(self, state):
for k in self.action_spaces:
self.action_spaces[k].reset(state)
def random_action(self):
"""Return randomly sampled action."""
random_action = {
k: self.action_spaces[k].random_action() for k in self._action_keys}
return random_action
def action_spec(self):
return self._action_spec
@property
def action_keys(self):
return list(self._action_keys)Classes
class Composite (**action_spaces)-
Composite action space.
Example usage: joystick_action_space = maze_action_space.Joystick( scaling_factor=0.1, action_layers='agent') eye_action_space = action_spaces.SetPosition( action_layers='eye_sprite')
action_space = action_spaces.Composite( joystick=joystick_action_space, eye=eye_action_space, ) # Playing the game... action = {'joystick': [0.1, -0.2], 'eye': [0.4, 0.8]} env.step(action=action) # goes to action_space.step(state, action)Constructor.
Args
action_spaces- Dict. Keys are strings and values are action spaces. An action is a dictionary with the same set of keys.
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class Composite(abstract_action_space.AbstractActionSpace): """Composite action space. Example usage: joystick_action_space = maze_action_space.Joystick( scaling_factor=0.1, action_layers='agent') eye_action_space = action_spaces.SetPosition( action_layers='eye_sprite') action_space = action_spaces.Composite( joystick=joystick_action_space, eye=eye_action_space, ) # Playing the game... action = {'joystick': [0.1, -0.2], 'eye': [0.4, 0.8]} env.step(action=action) # goes to action_space.step(state, action) """ def __init__(self, **action_spaces): """Constructor. Args: action_spaces: Dict. Keys are strings and values are action spaces. An action is a dictionary with the same set of keys. """ self.action_spaces = action_spaces self._action_keys = action_spaces.keys() self._action_spec = { key: value.action_spec() for key,value in self.action_spaces.items() } def step(self, state, action): """Apply action to environment state. Args: state: Ordereddict of layers of sprites. Environment state. action: Dict. Keys much be the same as self._action_keys. Each value will be fed into the action space of the corresponding key. """ for k, v in action.items(): self.action_spaces[k].step(state, v) def reset(self, state): for k in self.action_spaces: self.action_spaces[k].reset(state) def random_action(self): """Return randomly sampled action.""" random_action = { k: self.action_spaces[k].random_action() for k in self._action_keys} return random_action def action_spec(self): return self._action_spec @property def action_keys(self): return list(self._action_keys)Ancestors
- AbstractActionSpace
- abc.ABC
Instance variables
var action_keys-
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@property def action_keys(self): return list(self._action_keys)
Methods
def random_action(self)-
Return randomly sampled action.
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def random_action(self): """Return randomly sampled action.""" random_action = { k: self.action_spaces[k].random_action() for k in self._action_keys} return random_action def step(self, state, action)-
Apply action to environment state.
Args
state- Ordereddict of layers of sprites. Environment state.
action- Dict. Keys much be the same as self._action_keys. Each value will be fed into the action space of the corresponding key.
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def step(self, state, action): """Apply action to environment state. Args: state: Ordereddict of layers of sprites. Environment state. action: Dict. Keys much be the same as self._action_keys. Each value will be fed into the action space of the corresponding key. """ for k, v in action.items(): self.action_spaces[k].step(state, v)
Inherited members