<jupyter_start><jupyter_text>Lets the try ACER poly with ATARI EnvironmentACER- Sample Efficient Actor-Critic with Experience Replay (ACER). It combines several ideas of previous algorithms: it uses multiple workers (as A2C), implements a replay buffer (as in DQN), uses Retrace for Q-value estimation, importance sampling and a trust region.<jupyter_code># importing libraries

from stable_baselines.common.cmd_util import make_atari_env
from stable_baselines.common.policies import CnnPolicy
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.
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
# Stack 4 frames
env = VecFrameStack(env, n_stack=4)
# training the model

model = ACER(CnnPolicy, env, verbose=1)
model.learn(total_timesteps=10000)
# save the model 

model.save("acer_pong")
# load the model
trained_model = ACER.load("acer_pong", verbose=1)

# load the environment
env = make_atari_env('PongNoFrameskip-v4', num_env=4, seed=0)
env = VecFrameStack(env, n_stack=4)
trained_model.set_env(env)<jupyter_output>Loading a model without an environment, this model cannot be trained until it has a valid environment.
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