pacai.core.features
1import typing 2 3import pacai.core.action 4import pacai.core.agent 5import pacai.core.gamestate 6 7FeatureDict: typing.TypeAlias = dict[str, float] 8""" 9A collection of features where each feature is keyed by a string. 10This is a companion to WeightDict. 11 12This is a simple alias for a Python dict with string keys and float values. 13""" 14 15WeightDict: typing.TypeAlias = dict[str, float] 16""" 17A collection of weights where each weight is keyed by a string. 18This is a companion to FeatureDict. 19 20This is a simple alias for a Python dict with string keys and float values. 21""" 22 23@typing.runtime_checkable 24class FeatureExtractor(typing.Protocol): 25 """ 26 A function that can be used to extract features from a game state and action pair. 27 """ 28 29 def __call__(self, 30 state: pacai.core.gamestate.GameState, 31 action: pacai.core.action.Action, 32 agent: pacai.core.agent.Agent | None = None, 33 **kwargs: typing.Any) -> FeatureDict: 34 """ 35 Extract the features for the given state/action pair. 36 37 It is very important to note that we are not evaluating the current state, 38 we are evaluating the state/action pair. 39 A very common thing for extractors to do is to call generate_successor() on the state using the action, 40 and then evaluate the successor state. 41 This is a perfectly fine thing to do. 42 The state and action are provided here as separate values for performance reasons. 43 Extractors may be able to avoid copying states if they do not require a full successor. 44 45 The optional agent may be used as a means of passing persistent state 46 (like pre-computed distances) from the agent to this function. 47 """ 48 49def score_feature_extractor( 50 state: pacai.core.gamestate.GameState, 51 action: pacai.core.action.Action, 52 agent: pacai.core.agent.Agent | None = None, 53 **kwargs: typing.Any) -> FeatureDict: 54 """ 55 The most basic feature extractor, which just uses the state's current score. 56 57 Features: 58 - 'score' -- The current score of the game. 59 """ 60 61 rng = None 62 if (agent is not None): 63 rng = agent.rng 64 65 successor = state.generate_successor(action, rng = rng) 66 67 features = FeatureDict() 68 features['score'] = successor.score 69 70 return features 71 72def board_feature_extractor( 73 state: pacai.core.gamestate.GameState, 74 action: pacai.core.action.Action, 75 agent: pacai.core.agent.Agent | None = None, 76 **kwargs: typing.Any) -> FeatureDict: 77 """ 78 A feature extractor that just creates a key unique to the board/action pair and assigns it a value of 1.0. 79 It accomplishes this by generating a JSON string for all the non-wall objects in the board. 80 This will be slow, and should generally not be used outside of learning applications. 81 82 Features: 83 - '<action::board>' -- A key unique to each action/board pair. The value is always 1.0. 84 """ 85 86 key = f"{action}::{state.board.get_nonwall_string()}" 87 88 features = FeatureDict() 89 features[key] = 1.0 90 91 return features
FeatureDict: TypeAlias =
dict[str, float]
A collection of features where each feature is keyed by a string. This is a companion to WeightDict.
This is a simple alias for a Python dict with string keys and float values.
WeightDict: TypeAlias =
dict[str, float]
A collection of weights where each weight is keyed by a string. This is a companion to FeatureDict.
This is a simple alias for a Python dict with string keys and float values.
@typing.runtime_checkable
class
FeatureExtractor24@typing.runtime_checkable 25class FeatureExtractor(typing.Protocol): 26 """ 27 A function that can be used to extract features from a game state and action pair. 28 """ 29 30 def __call__(self, 31 state: pacai.core.gamestate.GameState, 32 action: pacai.core.action.Action, 33 agent: pacai.core.agent.Agent | None = None, 34 **kwargs: typing.Any) -> FeatureDict: 35 """ 36 Extract the features for the given state/action pair. 37 38 It is very important to note that we are not evaluating the current state, 39 we are evaluating the state/action pair. 40 A very common thing for extractors to do is to call generate_successor() on the state using the action, 41 and then evaluate the successor state. 42 This is a perfectly fine thing to do. 43 The state and action are provided here as separate values for performance reasons. 44 Extractors may be able to avoid copying states if they do not require a full successor. 45 46 The optional agent may be used as a means of passing persistent state 47 (like pre-computed distances) from the agent to this function. 48 """
A function that can be used to extract features from a game state and action pair.
FeatureExtractor(*args, **kwargs)
1953def _no_init_or_replace_init(self, *args, **kwargs): 1954 cls = type(self) 1955 1956 if cls._is_protocol: 1957 raise TypeError('Protocols cannot be instantiated') 1958 1959 # Already using a custom `__init__`. No need to calculate correct 1960 # `__init__` to call. This can lead to RecursionError. See bpo-45121. 1961 if cls.__init__ is not _no_init_or_replace_init: 1962 return 1963 1964 # Initially, `__init__` of a protocol subclass is set to `_no_init_or_replace_init`. 1965 # The first instantiation of the subclass will call `_no_init_or_replace_init` which 1966 # searches for a proper new `__init__` in the MRO. The new `__init__` 1967 # replaces the subclass' old `__init__` (ie `_no_init_or_replace_init`). Subsequent 1968 # instantiation of the protocol subclass will thus use the new 1969 # `__init__` and no longer call `_no_init_or_replace_init`. 1970 for base in cls.__mro__: 1971 init = base.__dict__.get('__init__', _no_init_or_replace_init) 1972 if init is not _no_init_or_replace_init: 1973 cls.__init__ = init 1974 break 1975 else: 1976 # should not happen 1977 cls.__init__ = object.__init__ 1978 1979 cls.__init__(self, *args, **kwargs)
def
score_feature_extractor( state: pacai.core.gamestate.GameState, action: pacai.core.action.Action, agent: pacai.core.agent.Agent | None = None, **kwargs: Any) -> dict[str, float]:
50def score_feature_extractor( 51 state: pacai.core.gamestate.GameState, 52 action: pacai.core.action.Action, 53 agent: pacai.core.agent.Agent | None = None, 54 **kwargs: typing.Any) -> FeatureDict: 55 """ 56 The most basic feature extractor, which just uses the state's current score. 57 58 Features: 59 - 'score' -- The current score of the game. 60 """ 61 62 rng = None 63 if (agent is not None): 64 rng = agent.rng 65 66 successor = state.generate_successor(action, rng = rng) 67 68 features = FeatureDict() 69 features['score'] = successor.score 70 71 return features
The most basic feature extractor, which just uses the state's current score.
Features:
- 'score' -- The current score of the game.
def
board_feature_extractor( state: pacai.core.gamestate.GameState, action: pacai.core.action.Action, agent: pacai.core.agent.Agent | None = None, **kwargs: Any) -> dict[str, float]:
73def board_feature_extractor( 74 state: pacai.core.gamestate.GameState, 75 action: pacai.core.action.Action, 76 agent: pacai.core.agent.Agent | None = None, 77 **kwargs: typing.Any) -> FeatureDict: 78 """ 79 A feature extractor that just creates a key unique to the board/action pair and assigns it a value of 1.0. 80 It accomplishes this by generating a JSON string for all the non-wall objects in the board. 81 This will be slow, and should generally not be used outside of learning applications. 82 83 Features: 84 - '<action::board>' -- A key unique to each action/board pair. The value is always 1.0. 85 """ 86 87 key = f"{action}::{state.board.get_nonwall_string()}" 88 89 features = FeatureDict() 90 features[key] = 1.0 91 92 return features
A feature extractor that just creates a key unique to the board/action pair and assigns it a value of 1.0. It accomplishes this by generating a JSON string for all the non-wall objects in the board. This will be slow, and should generally not be used outside of learning applications.
Features:
- '