Knapsack

Knapsack (Environment)

Knapsack environment as described in [1].

• observation: Observation

  • weights: jax array (float) of shape (num_items,) the weights of the items.
  • values: jax array (float) of shape (num_items,) the values of the items.
  • packed_items: jax array (bool) of shape (num_items,) binary mask denoting which items are already packed into the knapsack.
  • action_mask: jax array (bool) of shape (num_items,) binary mask denoting which items can be packed into the knapsack.

• action: jax array (int32) of shape () [0, ..., num_items - 1] -> item to pack.

• reward: jax array (float) of shape (), could be either:

  • dense: the value of the item to pack at the current timestep.
  • sparse: the sum of the values of the items packed in the bag at the end of the episode. In both cases, the reward is 0 if the action is invalid, i.e. an item that was previously selected is selected again or has a weight larger than the bag capacity.

• episode termination:

  • if no action can be performed, i.e. all items are packed or each remaining item's weight is larger than the bag capacity.
  • if an invalid action is taken, i.e. the chosen item is already packed or has a weight larger than the bag capacity.

• state: State

  • weights: jax array (float) of shape (num_items,) the weights of the items.
  • values: jax array (float) of shape (num_items,) the values of the items.
  • packed_items: jax array (bool) of shape (num_items,) binary mask denoting which items are already packed into the knapsack.
  • remaining_budget: jax array (float) the budget currently remaining.

[1] https://arxiv.org/abs/2010.16011

from jumanji.environments import Knapsack
env = Knapsack()
key = jax.random.PRNGKey(0)
state, timestep = jax.jit(env.reset)(key)
env.render(state)
action = env.action_spec.generate_value()
state, timestep = jax.jit(env.step)(state, action)
env.render(state)

observation_spec: jumanji.specs.Spec[jumanji.environments.packing.knapsack.types.Observation] cached property writable

Returns the observation spec.

Returns:

Type	Description
Spec for each field in the Observation	weights: BoundedArray (float) of shape (num_items,). values: BoundedArray (float) of shape (num_items,). packed_items: BoundedArray (bool) of shape (num_items,). action_mask: BoundedArray (bool) of shape (num_items,).

action_spec: DiscreteArray cached property writable

Returns the action spec.

Returns:

Type	Description
action_spec	a specs.DiscreteArray spec.

__init__(self, generator: Optional[jumanji.environments.packing.knapsack.generator.Generator] = None, reward_fn: Optional[jumanji.environments.packing.knapsack.reward.RewardFn] = None, viewer: Optional[jumanji.viewer.Viewer[jumanji.environments.packing.knapsack.types.State]] = None) special

Instantiates a Knapsack environment.

Parameters:

Name	Type	Description	Default
generator	Optional[jumanji.environments.packing.knapsack.generator.Generator]	Generator whose __call__ instantiates an environment instance. The default option is 'RandomGenerator' which samples Knapsack instances with 50 items and a total budget of 12.5.	None
reward_fn	Optional[jumanji.environments.packing.knapsack.reward.RewardFn]	RewardFn whose __call__ method computes the reward of an environment transition. The function must compute the reward based on the current state, the chosen action, the next state and whether the action is valid. Implemented options are [DenseReward, SparseReward]. Defaults to DenseReward.	None
viewer	Optional[jumanji.viewer.Viewer[jumanji.environments.packing.knapsack.types.State]]	Viewer used for rendering. Defaults to KnapsackViewer with "human" render mode.	None

reset(self, key: PRNGKeyArray) -> Tuple[jumanji.environments.packing.knapsack.types.State, jumanji.types.TimeStep[jumanji.environments.packing.knapsack.types.Observation]]

Resets the environment.

Parameters:

Name	Type	Description	Default
key	PRNGKeyArray	used to randomly generate the weights and values of the items.	required

Returns:

Type	Description
state	the new state of the environment. timestep: the first timestep returned by the environment.

step(self, state: State, action: Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number, float, int]) -> Tuple[jumanji.environments.packing.knapsack.types.State, jumanji.types.TimeStep[jumanji.environments.packing.knapsack.types.Observation]]

Run one timestep of the environment's dynamics.

Parameters:

Name	Type	Description	Default
state	State	State object containing the dynamics of the environment.	required
action	Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number, float, int]	index of next item to take.	required

Returns:

Type	Description
state	next state of the environment. timestep: the timestep to be observed.

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Last update: 2024-11-01