import sys
import os
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from agents.deep_q_agent import DeepQAgent
from gym.wrappers import ResizeObservation
from max_frameskip_env import MaxFrameskipEnv
from reward_cache_env import RewardCacheEnv
from penalize_death_env import PenalizeDeathEnv
import tensorflow as tf
import keras
env = gym_super_mario_bros.make('SuperMarioBros-v1')
env = JoypadSpace(env, SIMPLE_MOVEMENT)
env = RewardCacheEnv(env)
env = ResizeObservation(env, (64, 64))
env = PenalizeDeathEnv(env)
env = MaxFrameskipEnv(env)
config = tf.ConfigProto(device_count={'GPU': 1, 'CPU': 4})
sess = tf.Session(config=config)
keras.backend.set_session(sess)
#env = gym.make('SpaceInvaders-v0')
agent = DeepQAgent(env=env,
render_mode=None,
dueling_network=True,
prioritized_experience_replay=True)
agent.train()
parser = argparse.ArgumentParser(description=None)
# Question 1
# Change the var called environment_used to change the environment
parser.add_argument('env_id', nargs='?', default=environment_used, help='Select the environment to run')
args = parser.parse_args()
# You can set the level to logger.DEBUG or logger.WARN if you
# want to change the amount of output.
logger.set_level(logger.INFO)
env = gym.make(args.env_id)
if environment_used != "CartPole-v1":
env = GrayScaleObservation(env)
env = ResizeObservation(env, resolution)
env = FrameStack(env, 4)
agent = VizdoomAgent(env.action_space, resolution , eta, test_mode, environment_used)
else:
agent = RandomAgent(env.action_space, env.observation_space, eta, test_mode, environment_used)
# You provide the directory to write to (can be an existing
# directory, including one with existing data -- all monitor files
# will be namespaced). You can also dump to a tempdir if you'd
# like: tempfile.mkdtemp().
outdir = '/tmp/random-agent-results'
env = wrappers.Monitor(env, directory=outdir, force=True)
env.seed(0)
mem = Memory()
def make_env_all_params(rank, args):
"""Initialize the environment and apply wrappers.
Parameters
----------
rank :
Rank of the environment.
args :
Hyperparameters for this run.
Returns
-------
env
Environment with its individual wrappers.
"""
if args.env_kind == "atari":
from stable_baselines3.common.atari_wrappers import NoopResetEnv
from nupic.embodied.disagreement.envs.wrappers import (
AddRandomStateToInfo,
ExtraTimeLimit,
FrameStack,
MaxAndSkipEnv,
MontezumaInfoWrapper,
ProcessFrame84,
StickyActionEnv,
)
env = gym.make(args.env)
assert "NoFrameskip" in env.spec.id
if args.stickyAtari:
env._max_episode_steps = args.max_episode_steps * 4
env = StickyActionEnv(env)
else:
env = NoopResetEnv(env, noop_max=args.noop_max)
env = MaxAndSkipEnv(env, skip=4)
env = ProcessFrame84(env, crop=False)
env = FrameStack(env, 4)
if not args.stickyAtari:
env = ExtraTimeLimit(env, args.max_episode_steps)
if "Montezuma" in args.env:
env = MontezumaInfoWrapper(env)
env = AddRandomStateToInfo(env)
elif args.env_kind == "mario":
from nupic.embodied.disagreement.envs.wrappers import make_mario_env
env = make_mario_env()
elif args.env_kind == "retro_multi":
from nupic.embodied.disagreement.envs.wrappers import make_multi_pong
env = make_multi_pong()
elif args.env_kind == "roboarm":
from real_robots.envs import REALRobotEnv
from nupic.embodied.disagreement.envs.wrappers import CartesianControlDiscrete
env = REALRobotEnv(objects=3, action_type="cartesian")
env = CartesianControlDiscrete(
env,
crop_obs=args.crop_obs,
repeat=args.act_repeat,
touch_reward=args.touch_reward,
random_force=args.random_force,
)
if args.resize_obs > 0:
env = ResizeObservation(env, args.resize_obs)
print("adding monitor")
env = Monitor(env, filename=None)
return env