gfn.gym.box

Module Contents

Classes

Box

Box environment, corresponding to the one in Section 4.1 of https://arxiv.org/abs/2301.12594
class gfn.gym.box.Box(delta=0.1, R0=0.1, R1=0.5, R2=2.0, epsilon=0.0001, device_str='cpu')

Bases: gfn.env.Env

Box environment, corresponding to the one in Section 4.1 of https://arxiv.org/abs/2301.12594

Parameters

  • delta (float) –

  • R0 (float) –

  • R1 (float) –

  • R2 (float) –

  • epsilon (float) –

  • device_str (Literal[cpu, cuda]) –

property log_partition: float

Returns the logarithm of the partition function.

Return type

float

is_action_valid(states, actions, backward=False)

Returns True if the actions are valid in the given states.

Parameters
Return type

bool

log_reward(final_states)

Either this or reward needs to be implemented.

Parameters

final_states (gfn.states.States) –

Return type

torchtyping.TensorType[batch_shape, torch.float]

make_Actions_class()

Returns a class that inherits from Actions and implements the environment-specific methods.

Return type

type[gfn.actions.Actions]

make_States_class()

Returns a class that inherits from States and implements the environment-specific methods.

Return type

type[gfn.states.States]

maskless_backward_step(states, actions)

Function that takes a batch of states and actions and returns a batch of previous states. Does not need to check whether the actions are valid or the states are sink states.

Parameters
Return type

torchtyping.TensorType[batch_shape, 2, torch.float]

maskless_step(states, actions)

Function that takes a batch of states and actions and returns a batch of next states. Does not need to check whether the actions are valid or the states are sink states.

Parameters
Return type

torchtyping.TensorType[batch_shape, 2, torch.float]

static norm(x)
Parameters

x (torchtyping.TensorType[batch_shape, 2, torch.float]) –

Return type

torch.Tensor