def test_action_mask_run_acer(vec_env, policy, env_class):
env = vec_env([env_class])
model = ACER(policy, env, verbose=0)
obs, done, action_masks = env.reset(), [False], []
while not done[0]:
action, _states = model.predict(obs, action_mask=action_masks)
obs, _, done, infos = env.step(action)
action_masks.clear()
for info in infos:
env_action_mask = info.get('action_mask')
action_masks.append(env_action_mask)
env.close()
from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.vec_env import VecFrameStack
from stable_baselines import ACER
# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multiprocessing training (num_env=4 => 4 processes)
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
model = ACER('CnnPolicy', env, verbose=1)
model.learn(total_timesteps=25000)
# save
model.save("cnn_pong")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
mean_reward, std_reward = evaluate_policy(model,
env,
n_eval_episodes=20,
deterministic=False)
print('mean_reward = %s +/- %s' % (mean_reward, std_reward))
# Enjoy trained agent
env.rendermode = 'on'
obs = env.reset()
cumreward = 0
results = list()
minable = list()
while True:
#for i in range(turns):
action, _states = model.predict(obs, deterministic=False)
obs, rewards, dones, info = env.step(action)
cumreward += rewards
print(action, rewards, dones, cumreward)
results.append(info[0])
a = abs(info[1] -
1) #translating sequence errors to be positive, else zero
minable.append(a)
# if info[1]==1:
# results.append(info[0])
# a=abs(info[1]-1) #translating sequence errors to be positive, else zero
# minable.append(a)
#env.renderif('on')
if dones == True:
break
