def main():
arglist = parse_args()
N_episode = arglist.episodes
if arglist.render_mode:
N_episode = 1
reward_list = []
prey_reward_list = []
predator_reward_list = []
if arglist.game == "Pong-2p-v0":
with U.single_threaded_session():
model = make_model(arglist,
action_space=1,
scope="pong",
model_path=arglist.model_path,
load_model=arglist.use_model)
print('model size', model.param_count)
env = make_env(arglist)
U.initialize()
for i in range(N_episode):
reward, steps_taken = pong_simulate(model, env, arglist)
if arglist.render_mode:
print("terminal reward", reward, "average steps taken",
np.mean(steps_taken) + 1)
else:
pass
reward_list.append(reward[0])
if not arglist.render_mode:
print("seed", arglist.seed, "average_reward",
np.mean(reward_list), "stdev", np.std(reward_list))
if arglist.game == "prey_predator":
with U.single_threaded_session():
prey_model = make_model(arglist,
action_space=2,
scope="prey",
model_path=arglist.model_path,
load_model=arglist.use_model)
predator_model = make_model(arglist,
action_space=2,
scope="predator",
model_path=arglist.model_path,
load_model=arglist.use_model)
print('model size', prey_model.param_count,
predator_model.param_count)
env = make_env(arglist)
U.initialize()
for i in range(N_episode):
prey_reward, predator_reward, steps_taken = pp_simulate(
[prey_model, predator_model], env, arglist)
if arglist.render_mode:
print("terminal reward", rewards, "average steps taken",
np.mean(steps_taken) + 1)
else:
pass
prey_reward_list.append(prey_reward)
predator_reward_list.append(predator_reward)
if not arglist.render_mode:
print("Seed", arglist.seed, "prey average_reward",
np.mean(prey_reward_list), "predator average_reward",
np.mean(predator_reward_list))
def python_env(env=None, merge_env=None):
if merge_env is None:
merge_env = {}
global python_site_env
# Use site.addsitedir() to determine which search paths would be considered
# if 'third_party/python_packages' was a site-packages directory.
# PATH is also updated, so windows can find the DLLs that ship with pywin32.
if python_site_env is None:
python_site_env = {}
temp = os.environ["PATH"], sys.path
os.environ["PATH"], sys.path = "", []
site.addsitedir(os.path.join(libdeno_path,
"build")) # Modifies PATH and sys.path.
site.addsitedir(python_packages_path) # Modifies PATH and sys.path.
python_site_env = {"PATH": os.environ["PATH"], "PYTHONPATH": sys.path}
os.environ["PATH"], sys.path = temp
# Make a new environment object.
env = make_env(env=env, merge_env=merge_env)
# Apply PATH and PYTHONPATH from the site-packages environment.
add_env_path(python_site_env["PATH"], env=env, key="PATH")
add_env_path(python_site_env["PYTHONPATH"], env=env, key="PYTHONPATH")
return env
def google_env(env=None, merge_env={}, depot_tools_path=depot_tools_path):
env = make_env(env=env, merge_env=merge_env)
# Depot_tools to be in the PATH, before Python.
path_prefix = depot_tools_path + os.path.pathsep
if not env['PATH'].startswith(path_prefix):
env['PATH'] = path_prefix + env['PATH']
# We're not using Google's internal infrastructure.
if os.name == 'nt' and not 'DEPOT_TOOLS_WIN_TOOLCHAIN' in env:
env['DEPOT_TOOLS_WIN_TOOLCHAIN'] = "0"
return env
def python_env(env=None, merge_env=None):
if merge_env is None:
merge_env = {}
global python_site_env
# Use site.addsitedir() to determine which search paths would be considered
# if 'third_party/python_packages' was a site-packages directory.
# PATH is also updated, so windows can find the DLLs that ship with pywin32.
if python_site_env is None:
python_site_env = {}
temp = os.environ["PATH"], sys.path
os.environ["PATH"], sys.path = "", []
site.addsitedir(python_packages_path) # Modifies PATH and sys.path.
python_site_env = {"PATH": os.environ["PATH"], "PYTHONPATH": sys.path}
os.environ["PATH"], sys.path = temp
# Make a new environment object.
env = make_env(env=env, merge_env=merge_env)
# Apply PATH and PYTHONPATH from the site-packages environment.
add_env_path(python_site_env["PATH"], env=env, key="PATH")
add_env_path(python_site_env["PYTHONPATH"], env=env, key="PYTHONPATH")
return env
def google_env(env=None, merge_env={}, depot_tools_path=depot_tools_path):
env = make_env(env=env, merge_env=merge_env)
# Depot_tools to be in the PATH, before Python.
path_prefix = depot_tools_path + os.path.pathsep
if not env['PATH'].startswith(path_prefix):
env['PATH'] = path_prefix + env['PATH']
if os.name == "nt": # Windows-only enviroment tweaks.
# We're not using Google's internal infrastructure.
if os.name == "nt" and not "DEPOT_TOOLS_WIN_TOOLCHAIN" in env:
env["DEPOT_TOOLS_WIN_TOOLCHAIN"] = "0"
# The 'setup_toolchain.py' script does a good job finding the Windows
# SDK. Unfortunately, if any of the environment variables below are set
# (as vcvarsall.bat typically would), setup_toolchain absorbs them too,
# adding multiple identical -imsvc<path> items to CFLAGS.
# This small variation has no effect on compiler output, but it
# makes ninja rebuild everything, and causes sccache cache misses.
# TODO(piscisaureus): fix this upstream.
env["INCLUDE"] = ""
env["LIB"] = ""
env["LIBPATH"] = ""
return env
if __name__ == '__main__':
arglist = parse_args()
if not os.path.exists(arglist.data_dir):
os.makedirs(arglist.data_dir)
total_frames = 0
if arglist.game == "Pong-2p-v0":
with U.single_threaded_session():
model = make_model(arglist,
action_space=1,
scope="pong",
model_path=arglist.model_path,
load_model=arglist.use_model)
env = make_env(arglist, full_episode=True)
model.render_mode = arglist.render_mode
U.initialize()
for trial in range(arglist.max_trials): # 200 trials per worker
try:
random_generated_int = random.randint(0, 2**31 - 1)
filename = arglist.data_dir + "/" + str(
random_generated_int) + ".npz"
recording_obs = []
recording_action = []
recording_oppo_action = []
np.random.seed(random_generated_int)
env.seed(random_generated_int)
# random policy
model.init_random_model_params(stdev=np.random.rand() *
import numpy as np
from dqn_agent import DQNAgent
from util import make_env, plot_learning_curve
from gym import wrappers
if __name__ == "__main__":
env = make_env('PongNoFrameskip-v4')
best_score = -np.inf
load_checkpoint = True
n_games = 5
agent = DQNAgent(gamma=0.99, epsilon=0.1, lr=0.0001,
input_dims=(env.observation_space.shape), # we have modified the default env settings to get env.observation_space.shape
n_actions=env.action_space.n, mem_size=50000, eps_min=0.1,
batch_size=32, replace=1000, eps_dec=1e-5,
chkpt_dir='.\\models\\', algo='DQNAgent',
env_name='PongNoFrameskip-v4')
if load_checkpoint:
agent.load_models()
#env = wrappers.Monitor(env, '.\\video\\', video_callable=lambda episode_id: True, force=True)
fname= agent.algo+'_'+agent.env_name+'_lr'+str(agent.lr)+'_'+str(n_games)+'games'
figure_file = '.\\plots\\'+fname+'.png'
n_steps= 0
scores, eps_history, steps_array = [], [], []
for i in range(n_games):
done = False
score = 0
observation = env.reset() # reset() method is overloaded in class StackFrames(gym.ObservationWrapper) , observation.shape = (4, 84, 84)
# here (1, 84, 84) == (2, 84, 84) == (3, 84, 84) == (4, 84, 84)
