Spaces¶
Space¶
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class
rl_coach.spaces.
Space
(shape: Union[int, tuple, list, numpy.ndarray], low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf)[source]¶ A space defines a set of valid values
Parameters: - shape – the shape of the space
- low – the lowest values possible in the space. can be an array defining the lowest values per point, or a single value defining the general lowest values
- high – the highest values possible in the space. can be an array defining the highest values per point, or a single value defining the general highest values
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is_valid_index
(point: numpy.ndarray) → bool[source]¶ Checks if a given multidimensional point is within the bounds of the shape of the space
Parameters: point – a multidimensional point Returns: True if the point is within the shape of the space. False otherwise
Observation Spaces¶
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class
rl_coach.spaces.
ObservationSpace
(shape: Union[int, numpy.ndarray], low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf)[source]¶ -
is_valid_index
(point: numpy.ndarray) → bool¶ Checks if a given multidimensional point is within the bounds of the shape of the space
Parameters: point – a multidimensional point Returns: True if the point is within the shape of the space. False otherwise
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sample
() → numpy.ndarray¶ Sample the defined space, either uniformly, if space bounds are defined, or Normal distributed if no bounds are defined
Returns: A numpy array sampled from the space
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contains
(val: Union[int, float, numpy.ndarray]) → bool¶ Checks if the given value matches the space definition in terms of shape and values
Parameters: val – a value to check Returns: True / False depending on if the val matches the space definition
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VectorObservationSpace¶
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class
rl_coach.spaces.
VectorObservationSpace
(shape: int, low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf, measurements_names: List[str] = None)[source]¶ An observation space which is defined as a vector of elements. This can be particularly useful for environments which return measurements, such as in robotic environments.
PlanarMapsObservationSpace¶
Action Spaces¶
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class
rl_coach.spaces.
ActionSpace
(shape: Union[int, numpy.ndarray], low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf, descriptions: Union[None, List, Dict] = None, default_action: Union[int, float, numpy.ndarray, List] = None)[source]¶ -
clip_action_to_space
(action: Union[int, float, numpy.ndarray, List]) → Union[int, float, numpy.ndarray, List][source]¶ Given an action, clip its values to fit to the action space ranges
Parameters: action – a given action Returns: the clipped action
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is_valid_index
(point: numpy.ndarray) → bool¶ Checks if a given multidimensional point is within the bounds of the shape of the space
Parameters: point – a multidimensional point Returns: True if the point is within the shape of the space. False otherwise
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sample
() → numpy.ndarray¶ Sample the defined space, either uniformly, if space bounds are defined, or Normal distributed if no bounds are defined
Returns: A numpy array sampled from the space
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sample_with_info
() → rl_coach.core_types.ActionInfo[source]¶ Get a random action with additional “fake” info
Returns: An action info instance
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contains
(val: Union[int, float, numpy.ndarray]) → bool¶ Checks if the given value matches the space definition in terms of shape and values
Parameters: val – a value to check Returns: True / False depending on if the val matches the space definition
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AttentionActionSpace¶
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class
rl_coach.spaces.
AttentionActionSpace
(shape: int, low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf, descriptions: Union[None, List, Dict] = None, default_action: numpy.ndarray = None, forced_attention_size: Union[None, int, float, numpy.ndarray] = None)[source]¶ A box selection continuous action space, meaning that the actions are defined as selecting a multidimensional box from a given range. The actions will be in the form: [[low_x, low_y, …], [high_x, high_y, …]]
BoxActionSpace¶
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class
rl_coach.spaces.
BoxActionSpace
(shape: Union[int, numpy.ndarray], low: Union[None, int, float, numpy.ndarray] = -inf, high: Union[None, int, float, numpy.ndarray] = inf, descriptions: Union[None, List, Dict] = None, default_action: numpy.ndarray = None)[source]¶ A multidimensional bounded or unbounded continuous action space
DiscreteActionSpace¶
MultiSelectActionSpace¶
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class
rl_coach.spaces.
MultiSelectActionSpace
(size: int, max_simultaneous_selected_actions: int = 1, descriptions: Union[None, List, Dict] = None, default_action: numpy.ndarray = None, allow_no_action_to_be_selected=True)[source]¶ A discrete action space where multiple actions can be selected at once. The actions are encoded as multi-hot vectors
CompoundActionSpace¶
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class
rl_coach.spaces.
CompoundActionSpace
(sub_spaces: List[rl_coach.spaces.ActionSpace])[source]¶ An action space which consists of multiple sub-action spaces. For example, in Starcraft the agent should choose an action identifier from ~550 options (Discrete(550)), but it also needs to choose 13 different arguments for the selected action identifier, where each argument is by itself an action space. In Starcraft, the arguments are Discrete action spaces as well, but this is not mandatory.
Goal Spaces¶
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class
rl_coach.spaces.
GoalsSpace
(goal_name: str, reward_type: rl_coach.spaces.GoalToRewardConversion, distance_metric: Union[rl_coach.spaces.GoalsSpace.DistanceMetric, Callable])[source]¶ A multidimensional space with a goal type definition. It also behaves as an action space, so that hierarchical agents can use it as an output action space. The class acts as a wrapper to the target space. So after setting the target space, all the values of the class will match the values of the target space (the shape, low, high, etc.)
Parameters: - goal_name – the name of the observation space to use as the achieved goal.
- reward_type – the reward type to use for converting distances from goal to rewards
- distance_metric – the distance metric to use. could be either one of the distances in the DistanceMetric enum, or a custom function that gets two vectors as input and returns the distance between them
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clip_action_to_space
(action: Union[int, float, numpy.ndarray, List]) → Union[int, float, numpy.ndarray, List]¶ Given an action, clip its values to fit to the action space ranges
Parameters: action – a given action Returns: the clipped action
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distance_from_goal
(goal: numpy.ndarray, state: dict) → float[source]¶ Given a state, check its distance from the goal
Parameters: - goal – a numpy array representing the goal
- state – a dict representing the state
Returns: the distance from the goal
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get_reward_for_goal_and_state
(goal: numpy.ndarray, state: dict) → Tuple[float, bool][source]¶ Given a state, check if the goal was reached and return a reward accordingly
Parameters: - goal – a numpy array representing the goal
- state – a dict representing the state
Returns: the reward for the current goal and state pair and a boolean representing if the goal was reached
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goal_from_state
(state: Dict)[source]¶ Given a state, extract an observation according to the goal_name
Parameters: state – a dictionary of observations Returns: the observation corresponding to the goal_name
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is_valid_index
(point: numpy.ndarray) → bool¶ Checks if a given multidimensional point is within the bounds of the shape of the space
Parameters: point – a multidimensional point Returns: True if the point is within the shape of the space. False otherwise
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sample
() → numpy.ndarray¶ Sample the defined space, either uniformly, if space bounds are defined, or Normal distributed if no bounds are defined
Returns: A numpy array sampled from the space
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sample_with_info
() → rl_coach.core_types.ActionInfo¶ Get a random action with additional “fake” info
Returns: An action info instance
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contains
(val: Union[int, float, numpy.ndarray]) → bool¶ Checks if the given value matches the space definition in terms of shape and values
Parameters: val – a value to check Returns: True / False depending on if the val matches the space definition