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import sys
import gym
import matplotlib.pyplot as plt
from a3c.discrete_A3C import train
from a3c.utils import v_wrap
from wordle_env.wordle import WordleEnvBase
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.")
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'
env = gym.make(env_id)
global_ep, win_ep, gnet, res = train(env, max_ep)
print_results(global_ep, win_ep, res)
evaluate(gnet, env)
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