#env = Gridworld(width=6, height=6, cell_size=32, agent_pos=(0, 3), food_pos=[(0, 0), (3, 3), (4, 5), (2, 0)])
env_a = Gridworld(width=4,
height=4,
cell_size=32,
agent_pos=(0, 0),
food_pos=[(0, 3), (3, 3)])
env_b = Gridworld(width=4,
height=4,
cell_size=32,
agent_pos=(0, 0),
food_pos=[(0, 3), (3, 3)])
pg.init()
screen = pg.display.set_mode(
(env_a.cell_size * env_a.width, env_a.cell_size * env_a.height))
env_a.screen = screen
env_b.screen = screen
clock = pg.time.Clock()
def plot(history):
plt.figure(2)
plt.clf()
durations_t = torch.DoubleTensor(history)
plt.title('Training...')
plt.xlabel('Episode')
plt.ylabel('Duration')
plt.plot(durations_t.numpy(), c='lightgray', linewidth=1)
his = 50
if len(durations_t) >= his:
#env = Gridworld(width=6, height=6, cell_size=32, agent_pos=(0, 3), food_pos=[(0, 0), (3, 3), (4, 5), (2, 0)])
env_a = Gridworld(width=4,
height=4,
cell_size=32,
agent_pos=(0, 0),
food_pos=[(0, 3), (3, 3)])
env_b = Gridworld(width=4,
height=4,
cell_size=32,
agent_pos=(0, 0),
food_pos=[(0, 3), (3, 3)])
pg.init()
screen = pg.display.set_mode(
(env_a.cell_size * env_a.width, env_a.cell_size * env_a.height))
env_a.screen = screen
clock = pg.time.Clock()
def plot(history):
plt.figure(2)
plt.clf()
durations_t = torch.FloatTensor(history)
plt.title('Training...')
plt.xlabel('Episode')
plt.ylabel('Duration')
plt.plot(durations_t.numpy(), c='lightgray', linewidth=1)
his = 50
if len(durations_t) >= his:
means = durations_t.unfold(0, his, 1).mean(1).view(-1)
from gw_collect import Gridworld
import pygame as pg
for i in range(10):
env = Gridworld(width=4, height=4, cell_size=32, seed=i)
env.reset()
pg.init()
screen = pg.display.set_mode(
(env.cell_size * env.width, env.cell_size * env.height))
env.screen = screen
env.draw(screen)