Spaces:
Sleeping
Sleeping
File size: 1,311 Bytes
3cafd2c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
import torch
from .net import GreedyNet
from .utils import v_wrap
from wordle_env.state import update_from_mask
def suggest(
env,
words,
states,
pretrained_model_path
) -> str:
"""
Given a list of words and masks, return the next suggested word
:param agent:
:param env:
:param sequence: History of moves and outcomes until now
:return:
"""
n_s = env.observation_space.shape[0]
n_a = env.action_space.n
env = env.unwrapped
state = env.reset()
words_list = env.words
word_width = len(env.words[0])
net = GreedyNet(n_s, n_a, words_list, word_width)
net.load_state_dict(torch.load(pretrained_model_path))
for word, mask in zip(words, states):
word = word.upper()
mask = list(map(int, mask))
state = update_from_mask(state, word, mask)
return env.words[net.choose_action(v_wrap(state[None, :]))]
def play(net, env):
state = env.reset()
outcomes = []
win = False
for i in range(env.max_turns):
action = net.choose_action(v_wrap(state[None, :]))
state, reward, done, _ = env.step(action)
outcomes.append((env.words[action], reward))
if done:
if reward >= 0:
win = True
break
return win, outcomes
|