d3rlpy.dataset.Transition¶
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class
d3rlpy.dataset.Transition(observation_shape, action_size, observation, action, reward, next_observation, next_action, next_reward, terminal, prev_transition=None, next_transition=None)[source]¶ Transition class.
This class is designed to hold data between two time steps, which is usually used as inputs of loss calculation in reinforcement learning.
Parameters: - observation_shape (tuple) – observation shape.
- action_size (int) – dimension of action-space.
- observation (numpy.ndarray or torch.Tensor) – observation at t.
- action (numpy.ndarray or int) – action at t.
- reward (float) – reward at t.
- next_observation (numpy.ndarray or torch.Tensor) – observation at t+1.
- next_action (numpy.ndarray or int) – action at t+1.
- next_reward (float) – reward at t+1.
- terminal (int) – terminal flag at t+1.
- prev_transition (d3rlpy.dataset.Transition) – pointer to the previous transition.
- next_transition (d3rlpy.dataset.Transition) – pointer to the next transition.
Methods
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get_action_size()[source]¶ Returns dimension of action-space.
Returns: dimension of action-space. Return type: int
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get_observation_shape()[source]¶ Returns observation shape.
Returns: observation shape. Return type: tuple
Attributes
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action¶ Returns action at t.
Returns: action at t. Return type: (numpy.ndarray or int)
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next_action¶ Returns action at t+1.
Returns: action at t+1. Return type: (numpy.ndarray or int)
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next_observation¶ Returns observation at t+1.
Returns: observation at t+1. Return type: numpy.ndarray or torch.Tensor
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next_transition¶ Returns pointer to the next transition.
If this is the last transition, this method should return
None.Returns: next transition. Return type: d3rlpy.dataset.Transition
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observation¶ Returns observation at t.
Returns: observation at t. Return type: numpy.ndarray or torch.Tensor
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prev_transition¶ Returns pointer to the previous transition.
If this is the first transition, this method should return
None.Returns: previous transition. Return type: d3rlpy.dataset.Transition