--- /dev/null
+#!/usr/bin/env python
+
+#########################################################################
+# This program is free software: you can redistribute it and/or modify #
+# it under the terms of the version 3 of the GNU General Public License #
+# as published by the Free Software Foundation. #
+# #
+# This program is distributed in the hope that it will be useful, but #
+# WITHOUT ANY WARRANTY; without even the implied warranty of #
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU #
+# General Public License for more details. #
+# #
+# You should have received a copy of the GNU General Public License #
+# along with this program. If not, see <http://www.gnu.org/licenses/>. #
+# #
+# Written by and Copyright (C) Francois Fleuret #
+# Contact <francois.fleuret@unige.ch> for comments & bug reports #
+#########################################################################
+
+# This is a tiny rogue-like environment implemented with tensor
+# operations, that runs in batches efficiently on a GPU. On a RTX4090
+# it can initialize ~20k environments per second and run ~40k
+# iterations.
+#
+# The agent "@" moves in a maze-like grid with random walls "#". There
+# are five actions: move NESW or do not move.
+#
+# There are monsters "$" moving randomly. The agent gets hit by every
+# monster present in one of the 4 direct neighborhoods at the end of
+# the moves, each hit results in a rewards of -1.
+#
+# The agent starts with 5 life points, each hit costs it 1pt, when it
+# gets to 0 it dies, gets a reward of -10 and the episode is over. At
+# every step it recovers 1/20th of a life point, with a maximum of
+# 5pt.
+#
+# The agent can carry "keys" ("a", "b", "c") that open "vaults" ("A",
+# "B", "C"). They keys can only be used in sequence: initially the
+# agent can move only to free spaces, or to the "a", in which case it
+# now carries it, and can move to free spaces or the "A". When it
+# moves to the "A", it gets a reward and loses the "a", but can now
+# move to the "b", etc. Rewards are 1 for "A" and "B" and 10 for "C".
+
+######################################################################
+
+import torch
+
+from torch.nn.functional import conv2d
+
+######################################################################
+
+
+class PicroCrafterEngine:
+ def __init__(
+ self,
+ world_height=27,
+ world_width=27,
+ nb_walls=27,
+ margin=2,
+ view_height=5,
+ view_width=5,
+ device=torch.device("cpu"),
+ ):
+ assert (world_height - 2 * margin) % (view_height - 2 * margin) == 0
+ assert (world_width - 2 * margin) % (view_width - 2 * margin) == 0
+
+ self.device = device
+
+ self.world_height = world_height
+ self.world_width = world_width
+ self.margin = margin
+ self.view_height = view_height
+ self.view_width = view_width
+ self.nb_walls = nb_walls
+ self.life_level_max = 5
+ self.life_level_gain_100th = 5
+ self.reward_per_hit = -1
+ self.reward_death = -10
+
+ self.tokens = " +#@$aAbBcC"
+ self.token2id = dict([(t, n) for n, t in enumerate(self.tokens)])
+ self.id2token = dict([(n, t) for n, t in enumerate(self.tokens)])
+
+ self.next_object = dict(
+ [
+ (self.token2id[s], self.token2id[t])
+ for (s, t) in [
+ ("a", "A"),
+ ("A", "b"),
+ ("b", "B"),
+ ("B", "c"),
+ ("c", "C"),
+ ]
+ ]
+ )
+
+ self.object_reward = dict(
+ [
+ (self.token2id[t], r)
+ for (t, r) in [
+ ("a", 0),
+ ("A", 1),
+ ("b", 0),
+ ("B", 1),
+ ("c", 0),
+ ("C", 10),
+ ]
+ ]
+ )
+
+ self.acessible_object_to_inventory = dict(
+ [
+ (self.token2id[s], self.token2id[t])
+ for (s, t) in [
+ ("a", " "),
+ ("A", "a"),
+ ("b", " "),
+ ("B", "b"),
+ ("c", " "),
+ ("C", " "),
+ ]
+ ]
+ )
+
+ def reset(self, nb_agents):
+ self.worlds = self.create_worlds(
+ nb_agents, self.world_height, self.world_width, self.nb_walls, self.margin
+ ).to(self.device)
+ self.life_level_in_100th = torch.full(
+ (nb_agents,), self.life_level_max * 100, device=self.device
+ )
+ self.accessible_object = torch.full(
+ (nb_agents,), self.token2id["a"], device=self.device
+ )
+
+ def create_mazes(self, nb, height, width, nb_walls):
+ m = torch.zeros(nb, height, width, dtype=torch.int64, device=self.device)
+ m[:, 0, :] = 1
+ m[:, -1, :] = 1
+ m[:, :, 0] = 1
+ m[:, :, -1] = 1
+
+ i = torch.arange(height, device=m.device)[None, :, None]
+ j = torch.arange(width, device=m.device)[None, None, :]
+
+ for _ in range(nb_walls):
+ q = torch.rand(m.size(), device=m.device).flatten(1).sort(-1).indices * (
+ (1 - m) * (i % 2 == 0) * (j % 2 == 0)
+ ).flatten(1)
+ q = (q == q.max(dim=-1, keepdim=True).values).long().view(m.size())
+ a = q[:, None].expand(-1, 4, -1, -1).clone()
+ a[:, 0, :-1, :] += q[:, 1:, :]
+ a[:, 0, :-2, :] += q[:, 2:, :]
+ a[:, 1, 1:, :] += q[:, :-1, :]
+ a[:, 1, 2:, :] += q[:, :-2, :]
+ a[:, 2, :, :-1] += q[:, :, 1:]
+ a[:, 2, :, :-2] += q[:, :, 2:]
+ a[:, 3, :, 1:] += q[:, :, :-1]
+ a[:, 3, :, 2:] += q[:, :, :-2]
+ a = a[
+ torch.arange(a.size(0), device=a.device),
+ torch.randint(4, (a.size(0),), device=a.device),
+ ]
+ m = (m + q + a).clamp(max=1)
+
+ return m
+
+ def create_worlds(self, nb, height, width, nb_walls, margin=2):
+ margin -= 1 # The maze adds a wall all around
+ m = self.create_mazes(nb, height - 2 * margin, width - 2 * margin, nb_walls)
+ q = m.flatten(1)
+ z = "@aAbBcC$$$$$" # What to add to the maze
+ u = torch.rand(q.size(), device=q.device) * (1 - q)
+ r = u.sort(dim=-1, descending=True).indices[:, : len(z)]
+
+ q *= self.token2id["#"]
+ q[
+ torch.arange(q.size(0), device=q.device)[:, None].expand_as(r), r
+ ] = torch.tensor([self.token2id[c] for c in z], device=q.device)[None, :]
+
+ if margin > 0:
+ r = m.new_full(
+ (m.size(0), m.size(1) + margin * 2, m.size(2) + margin * 2),
+ self.token2id["+"],
+ )
+ r[:, margin:-margin, margin:-margin] = m
+ m = r
+ return m
+
+ def nb_actions(self):
+ return 5
+
+ def nb_view_tokens(self):
+ return len(self.tokens)
+
+ def min_max_reward(self):
+ return (
+ min(4 * self.reward_per_hit, self.reward_death),
+ max(self.object_reward.values()),
+ )
+
+ def step(self, actions):
+ a = (self.worlds == self.token2id["@"]).nonzero()
+ self.worlds[a[:, 0], a[:, 1], a[:, 2]] = self.token2id[" "]
+ s = torch.tensor([[0, 0], [-1, 0], [0, 1], [1, 0], [0, -1]], device=self.device)
+ b = a.clone()
+ b[:, 1:] = b[:, 1:] + s[actions[b[:, 0]]]
+
+ # position is empty
+ o = (self.worlds[b[:, 0], b[:, 1], b[:, 2]] == self.token2id[" "]).long()
+ # or it is the next accessible object
+ q = (
+ self.worlds[b[:, 0], b[:, 1], b[:, 2]] == self.accessible_object[b[:, 0]]
+ ).long()
+ o = (o + q).clamp(max=1)[:, None]
+ b = (1 - o) * a + o * b
+ self.worlds[b[:, 0], b[:, 1], b[:, 2]] = self.token2id["@"]
+
+ nb_hits = self.monster_moves()
+
+ alive_before = self.life_level_in_100th > 0
+ self.life_level_in_100th[alive_before] = (
+ self.life_level_in_100th[alive_before]
+ + self.life_level_gain_100th
+ - nb_hits[alive_before] * 100
+ ).clamp(max=self.life_level_max * 100)
+ alive_after = self.life_level_in_100th > 0
+ self.worlds[torch.logical_not(alive_after)] = self.token2id["#"]
+ reward = nb_hits * self.reward_per_hit
+
+ for i in range(q.size(0)):
+ if q[i] == 1:
+ reward[i] += self.object_reward[self.accessible_object[i].item()]
+ self.accessible_object[i] = self.next_object[
+ self.accessible_object[i].item()
+ ]
+
+ reward = (
+ reward + alive_before.long() * (1 - alive_after.long()) * self.reward_death
+ )
+ inventory = torch.tensor(
+ [
+ self.acessible_object_to_inventory[s.item()]
+ for s in self.accessible_object
+ ]
+ )
+
+ reward[torch.logical_not(alive_before)] = 0
+ return reward, inventory, self.life_level_in_100th // 100
+
+ def monster_moves(self):
+ # Current positions of the monsters
+ m = (self.worlds == self.token2id["$"]).long().flatten(1)
+
+ # Total number of monsters
+ n = m.sum(-1).max()
+
+ # Create a tensor with one channel per monster
+ r = (
+ (torch.rand(m.size(), device=m.device) * m)
+ .sort(dim=-1, descending=True)
+ .indices[:, :n]
+ )
+ o = m.new_zeros((m.size(0), n) + m.size()[1:])
+ i = torch.arange(o.size(0), device=o.device)[:, None].expand(-1, o.size(1))
+ j = torch.arange(o.size(1), device=o.device)[None, :].expand(o.size(0), -1)
+ o[i, j, r] = 1
+ o = o * m[:, None]
+
+ # Create the tensor of possible motions
+ o = o.view((self.worlds.size(0), n) + self.worlds.flatten(1).size()[1:])
+ move_kernel = torch.tensor(
+ [[[[0.0, 1.0, 0.0], [1.0, 1.0, 1.0], [0.0, 1.0, 0.0]]]], device=o.device
+ )
+
+ p = (
+ conv2d(
+ o.view(
+ o.size(0) * o.size(1), 1, self.worlds.size(-2), self.worlds.size(-1)
+ ).float(),
+ move_kernel,
+ padding=1,
+ ).view(o.size())
+ == 1.0
+ ).long()
+
+ # Let's do the moves per say
+ i = torch.arange(self.worlds.size(0), device=self.worlds.device)[
+ :, None
+ ].expand_as(r)
+
+ for n in range(p.size(1)):
+ u = o[:, n].sort(dim=-1, descending=True).indices[:, :1]
+ q = p[:, n] * (self.worlds.flatten(1) == self.token2id[" "]) + o[:, n]
+ r = (
+ (q * torch.rand(q.size(), device=q.device))
+ .sort(dim=-1, descending=True)
+ .indices[:, :1]
+ )
+ self.worlds.flatten(1)[i, u] = self.token2id[" "]
+ self.worlds.flatten(1)[i, r] = self.token2id["$"]
+
+ nb_hits = (
+ (
+ conv2d(
+ (self.worlds == self.token2id["$"]).float()[:, None],
+ move_kernel,
+ padding=1,
+ )
+ .long()
+ .squeeze(1)
+ * (self.worlds == self.token2id["@"]).long()
+ )
+ .flatten(1)
+ .sum(-1)
+ )
+
+ return nb_hits
+
+ def views(self):
+ i_height, i_width = (
+ self.view_height - 2 * self.margin,
+ self.view_width - 2 * self.margin,
+ )
+ a = (self.worlds == self.token2id["@"]).nonzero()
+ y = i_height * ((a[:, 1] - self.margin) // i_height)
+ x = i_width * ((a[:, 2] - self.margin) // i_width)
+ n = a[:, 0][:, None, None].expand(-1, self.view_height, self.view_width)
+ i = (
+ torch.arange(self.view_height, device=a.device)[None, :, None]
+ + y[:, None, None]
+ ).expand_as(n)
+ j = (
+ torch.arange(self.view_width, device=a.device)[None, None, :]
+ + x[:, None, None]
+ ).expand_as(n)
+ v = self.worlds.new_full(
+ (self.worlds.size(0), self.view_height, self.view_width), self.token2id["#"]
+ )
+
+ v[a[:, 0]] = self.worlds[n, i, j]
+
+ return v
+
+ def print_worlds(
+ self, src=None, comments=[], width=None, printer=print, ansi_term=False
+ ):
+ if src is None:
+ src = self.worlds
+
+ if width is None:
+ width = src.size(2)
+
+ def token(n):
+ n = n.item()
+ if n in self.id2token:
+ return self.id2token[n]
+ else:
+ return "?"
+
+ for k in range(src.size(1)):
+ s = ["".join([token(n) for n in m[k]]) for m in src]
+ s = [r + " " * (width - len(r)) for r in s]
+ if ansi_term:
+
+ def colorize(x):
+ for u, c in [("#", 40), ("$", 31), ("@", 32)] + [
+ (x, 36) for x in "aAbBcC"
+ ]:
+ x = x.replace(u, f"\u001b[{c}m{u}\u001b[0m")
+ return x
+
+ s = [colorize(x) for x in s]
+ printer(" | ".join(s))
+
+ s = [c + " " * (width - len(c)) for c in comments]
+ printer(" | ".join(s))
+
+
+######################################################################
+
+if __name__ == "__main__":
+ import os, time
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ ansi_term = False
+ # nb_agents, nb_iter, display = 1000, 100, False
+ nb_agents, nb_iter, display = 3, 10000, True
+ ansi_term = True
+
+ start_time = time.perf_counter()
+ engine = PicroCrafterEngine(
+ world_height=27,
+ world_width=27,
+ nb_walls=35,
+ view_height=9,
+ view_width=9,
+ margin=4,
+ device=device,
+ )
+
+ engine.reset(nb_agents)
+
+ print(f"timing {nb_agents/(time.perf_counter() - start_time)} init per s")
+
+ start_time = time.perf_counter()
+
+ for k in range(nb_iter):
+ action = torch.randint(engine.nb_actions(), (nb_agents,), device=device)
+ rewards, inventories, life_levels = engine.step(
+ torch.randint(engine.nb_actions(), (nb_agents,), device=device)
+ )
+
+ if display:
+ os.system("clear")
+ engine.print_worlds(
+ ansi_term=ansi_term,
+ )
+ print()
+ engine.print_worlds(
+ src=engine.views(),
+ comments=[
+ f"L{p}I{engine.id2token[s.item()]}R{r}"
+ for p, s, r in zip(life_levels, inventories, rewards)
+ ],
+ width=engine.world_width,
+ ansi_term=ansi_term,
+ )
+ time.sleep(0.5)
+
+ if (life_levels > 0).long().sum() == 0:
+ break
+
+ print(
+ f"timing {(nb_agents*nb_iter)/(time.perf_counter() - start_time)} iteration per s"
+ )
projects / pytorch.git / commitdiff