Spaces:
Sleeping
Sleeping
File size: 2,729 Bytes
f05ece6 676caef 44db2f9 676caef 44db2f9 1bd428f 350e00d 676caef 44db2f9 676caef 44db2f9 350e00d 1bd428f 350e00d 676caef 350e00d 1bd428f 62c6c3b 44db2f9 350e00d 1bd428f 676caef 1bd428f 676caef |
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 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import sys
import os
import gym
import torch
import matplotlib.pyplot as plt
from a3c.discrete_A3C import train
from a3c.utils import v_wrap
from a3c.net import Net
from wordle_env.wordle import WordleEnvBase
def evaluate_checkpoints(dir, env):
n_s = env.observation_space.shape[0]
n_a = env.action_space.n
words_list = env.words
word_width = len(env.words[0])
net = Net(n_s, n_a, words_list, word_width)
results = {}
print(dir)
for checkpoint in os.listdir(dir):
checkpoint_path = os.path.join(dir, checkpoint)
if os.path.isfile(checkpoint_path):
net.load_state_dict(torch.load(checkpoint_path))
wins, guesses = evaluate(net, env)
results[checkpoint] = wins, guesses
return dict(sorted(results.items(), key=lambda x: (x[1][0], -x[1][1]), reverse=True))
def evaluate(net, env):
print("Evaluation mode")
n_wins = 0
n_guesses = 0
n_win_guesses = 0
env = env.unwrapped
N = env.allowable_words
for goal_word in env.words[:N]:
win, outcomes = play(net, env)
if win:
n_wins += 1
n_win_guesses += len(outcomes)
# else:
# print("Lost!", goal_word, outcomes)
n_guesses += len(outcomes)
print(f"Evaluation complete, won {n_wins/N*100}% and took {n_win_guesses/n_wins} guesses per win, "
f"{n_guesses / N} including losses.")
return n_wins/N*100, n_win_guesses/n_wins
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
def print_results(global_ep, win_ep, res):
print("Jugadas:", global_ep.value)
print("Ganadas:", win_ep.value)
plt.plot(res)
plt.ylabel('Moving average ep reward')
plt.xlabel('Step')
plt.show()
if __name__ == "__main__":
max_ep = int(sys.argv[1]) if len(sys.argv) > 1 else 100000
env_id = sys.argv[2] if len(sys.argv) > 2 else 'WordleEnv100FullAction-v0'
evaluation = True if len(sys.argv) > 3 and sys.argv[3] == 'evaluation' else False
env = gym.make(env_id)
model_checkpoint_dir = os.path.join('checkpoints', env.unwrapped.spec.id)
if not evaluation:
global_ep, win_ep, gnet, res = train(env, max_ep, model_checkpoint_dir)
print_results(global_ep, win_ep, res)
evaluate(gnet, env)
else:
results = evaluate_checkpoints(model_checkpoint_dir, env)
print(results)
|