from typing import Optional
import torch
from tensordict.tensordict import TensorDict
from torchrl.data import (
BoundedTensorSpec,
CompositeSpec,
UnboundedContinuousTensorSpec,
UnboundedDiscreteTensorSpec,
)
from rl4co.envs.common.base import RL4COEnvBase
from rl4co.envs.common.utils import batch_to_scalar
from rl4co.utils.ops import gather_by_index, get_tour_length
from rl4co.utils.pylogger import get_pylogger
log = get_pylogger(__name__)
[docs]class TSPEnv(RL4COEnvBase):
"""
Traveling Salesman Problem environment
At each step, the agent chooses a city to visit. The reward is the -infinite unless the agent visits all the cities.
In that case, the reward is (-)length of the path: maximizing the reward is equivalent to minimizing the path length.
Args:
num_loc: number of locations (cities) in the TSP
td_params: parameters of the environment
seed: seed for the environment
device: device to use. Generally, no need to set as tensors are updated on the fly
"""
name = "tsp"
def __init__(
self,
num_loc: int = 20,
min_loc: float = 0,
max_loc: float = 1,
td_params: TensorDict = None,
**kwargs,
):
super().__init__(**kwargs)
self.num_loc = num_loc
self.min_loc = min_loc
self.max_loc = max_loc
self._make_spec(td_params)
@staticmethod
def _step(td: TensorDict) -> TensorDict:
current_node = td["action"]
first_node = current_node if batch_to_scalar(td["i"]) == 0 else td["first_node"]
# Set not visited to 0 (i.e., we visited the node)
available = td["action_mask"].scatter(
-1, current_node.unsqueeze(-1).expand_as(td["action_mask"]), 0
)
# We are done there are no unvisited locations
done = torch.count_nonzero(available, dim=-1) <= 0
# The reward is calculated outside via get_reward for efficiency, so we set it to -inf here
reward = torch.ones_like(done) * float("-inf")
# The output must be written in a ``"next"`` entry
return TensorDict(
{
"next": {
"locs": td["locs"],
"first_node": first_node,
"current_node": current_node,
"i": td["i"] + 1,
"action_mask": available,
"reward": reward,
"done": done,
}
},
td.shape,
)
def _reset(self, td: Optional[TensorDict] = None, batch_size=None) -> TensorDict:
# Initialize locations
init_locs = td["locs"] if td is not None else None
if batch_size is None:
batch_size = self.batch_size if init_locs is None else init_locs.shape[:-2]
self.device = device = init_locs.device if init_locs is not None else self.device
if init_locs is None:
init_locs = self.generate_data(batch_size=batch_size).to(device)["locs"]
batch_size = [batch_size] if isinstance(batch_size, int) else batch_size
# We do not enforce loading from self for flexibility
num_loc = init_locs.shape[-2]
# Other variables
current_node = torch.zeros((batch_size), dtype=torch.int64, device=device)
available = torch.ones(
(*batch_size, num_loc), dtype=torch.bool, device=device
) # 1 means not visited, i.e. action is allowed
i = torch.zeros((*batch_size, 1), dtype=torch.int64, device=device)
return TensorDict(
{
"locs": init_locs,
"first_node": current_node,
"current_node": current_node,
"i": i,
"action_mask": available,
},
batch_size=batch_size,
)
def _make_spec(self, td_params):
"""Make the observation and action specs from the parameters"""
self.observation_spec = CompositeSpec(
locs=BoundedTensorSpec(
minimum=self.min_loc,
maximum=self.max_loc,
shape=(self.num_loc, 2),
dtype=torch.float32,
),
first_node=UnboundedDiscreteTensorSpec(
shape=(1),
dtype=torch.int64,
),
current_node=UnboundedDiscreteTensorSpec(
shape=(1),
dtype=torch.int64,
),
i=UnboundedDiscreteTensorSpec(
shape=(1),
dtype=torch.int64,
),
action_mask=UnboundedDiscreteTensorSpec(
shape=(self.num_loc),
dtype=torch.bool,
),
shape=(),
)
self.input_spec = self.observation_spec.clone()
self.action_spec = BoundedTensorSpec(
shape=(1,),
dtype=torch.int64,
minimum=0,
maximum=self.num_loc,
)
self.reward_spec = UnboundedContinuousTensorSpec(shape=(1,))
self.done_spec = UnboundedDiscreteTensorSpec(shape=(1,), dtype=torch.bool)
[docs] @staticmethod
def get_reward(td, actions) -> TensorDict:
locs = td["locs"]
assert (
torch.arange(actions.size(1), out=actions.data.new())
.view(1, -1)
.expand_as(actions)
== actions.data.sort(1)[0]
).all(), "Invalid tour"
# Gather locations in order of tour and return distance between them (i.e., -reward)
locs_ordered = gather_by_index(locs, actions)
return -get_tour_length(locs_ordered)
[docs] def generate_data(self, batch_size) -> TensorDict:
batch_size = [batch_size] if isinstance(batch_size, int) else batch_size
locs = (
torch.rand((*batch_size, self.num_loc, 2), generator=self.rng)
* (self.max_loc - self.min_loc)
+ self.min_loc
)
return TensorDict({"locs": locs}, batch_size=batch_size)
[docs] @staticmethod
def render(td, actions=None, ax=None):
import matplotlib.pyplot as plt
import numpy as np
if ax is None:
# Create a plot of the nodes
_, ax = plt.subplots()
td = td.detach().cpu()
if actions is None:
actions = td.get("action", None)
# if batch_size greater than 0 , we need to select the first batch element
if td.batch_size != torch.Size([]):
td = td[0]
actions = actions[0]
locs = td["locs"]
# gather locs in order of action if available
if actions is None:
log.warning("No action in TensorDict, rendering unsorted locs")
else:
actions = actions.detach().cpu()
locs = gather_by_index(locs, actions, dim=0)
# Cat the first node to the end to complete the tour
locs = torch.cat((locs, locs[0:1]))
x, y = locs[:, 0], locs[:, 1]
# Plot the visited nodes
ax.scatter(x, y, color="tab:blue")
# Add arrows between visited nodes as a quiver plot
dx, dy = np.diff(x), np.diff(y)
ax.quiver(
x[:-1], y[:-1], dx, dy, scale_units="xy", angles="xy", scale=1, color="k"
)
# Setup limits and show
ax.set_xlim(-0.05, 1.05)
ax.set_ylim(-0.05, 1.05)
plt.show()
