population_size = 128
total_tournaments = 100000
save_freq = 1000
def mutate(length, mutation_rate, mutation_sigma):
# (not used, in case I wanted to do partial mutations)
# create an additive mutation vector of some size
mask = np.random.randint(int(1/mutation_rate), size=length)
mask = 1-np.minimum(mask, 1)
noise = np.random.normal(size=length) * mutation_sigma
return mask * noise
# Log results
from library import util
args = util.get_args('ga_selfplay_obs', '../zoo/ga/_sp/ga.json')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
with open(BEST_MODEL_PATH) as f:
data = json.load(f)
model_params = np.array(data[0])
# Create two instances of a feed forward policy we may need later.
policy_left = Model(mlp.games['slimevolleylite'])
policy_right = Model(mlp.games['slimevolleylite'])
param_count = policy_left.param_count
print("Number of parameters of the neural net policy:", param_count) # 273 for slimevolleylite
save_freq = 1000
def mutate(length, mutation_rate, mutation_sigma):
# (not used, in case I wanted to do partial mutations)
# create an additive mutation vector of some size
mask = np.random.randint(int(1 / mutation_rate), size=length)
mask = 1 - np.minimum(mask, 1)
noise = np.random.normal(size=length) * mutation_sigma
return mask * noise
# Log results
from library import util
args = util.get_args('ga_selfplay_actionbit', '../zoo/ga/_sp/ga.json')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
with open(BEST_MODEL_PATH) as f:
data = json.load(f)
model_params = np.array(data[0])
# Create two instances of a feed forward policy we may need later.
policy_left = Model(mlp.games['slimevolleylite'])
policy_right = Model(mlp.games['slimevolleylite'])
param_count = policy_left.param_count
print("Number of parameters of the neural net policy:",
param_count) # 273 for slimevolleylite
save_freq = 1000
def mutate(length, mutation_rate, mutation_sigma):
# (not used, in case I wanted to do partial mutations)
# create an additive mutation vector of some size
mask = np.random.randint(int(1 / mutation_rate), size=length)
mask = 1 - np.minimum(mask, 1)
noise = np.random.normal(size=length) * mutation_sigma
return mask * noise
# Log results
from library import util
args = util.get_args('ga_selfplay_constant', '../zoo/ga/_sp/ga.json')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
with open(BEST_MODEL_PATH) as f:
data = json.load(f)
model_params = np.array(data[0])
# Create two instances of a feed forward policy we may need later.
policy_left = Model(mlp.games['slimevolleylite'])
policy_right = Model(mlp.games['slimevolleylite'])
param_count = policy_left.param_count
print("Number of parameters of the neural net policy:",
param_count) # 273 for slimevolleylite
from mpi4py import MPI
from stable_baselines.common import set_global_seeds
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import bench, logger, PPO1
from stable_baselines.common.callbacks import EvalCallback
NUM_TIMESTEPS = int(2e6)
SEED = 831
EVAL_FREQ = 200000
EVAL_EPISODES = 1000
# Log results and get trained model location
from library import util
args = util.get_args(logdir='ppo_mpi_spike',
modelpath='../zoo/ppo/best_model.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
PPO_PATH = args.modelpath
from library import wrapper
def make_env(seed):
# Add spike wrapper to environment
env = wrapper.SpikeWrapper(gym.make("SlimeVolley-v0"))
env.seed(seed)
return env
from stable_baselines.common import set_global_seeds
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import bench, logger, PPO1
from stable_baselines.common.callbacks import EvalCallback
from library.Observation_Wrapper import ObservationWrapper
NUM_TIMESTEPS = int(2e6)
SEED = 831
EVAL_FREQ = 200000
EVAL_EPISODES = 1000
# Log results
from library import util
args = util.get_args('ppo_extended_obs', '../zoo/ppo/best_model.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
if not os.path.exists(BEST_MODEL_PATH):
raise Exception('File does not exist:', BEST_MODEL_PATH)
def make_env(seed):
env = ObservationWrapper(gym.make("SlimeVolley-v0"))
env.seed(seed)
return env
from library.Observation_Wrapper import ObservationWrapperHalf
# Settings
SEED = 17
NUM_TIMESTEPS = int(2e6)
EVAL_FREQ = int(1e5)
EVAL_EPISODES = int(1e2)
BEST_THRESHOLD = 0.5 # must achieve a mean score above this to replace prev best self
RENDER_MODE = False # set this to false if you plan on running for full 1000 trials.
# Log results
from library import util
args = util.get_args('ppo_selfplay_extended_obs_half',
'../zoo/ppo_sp/history_00000144.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
if not os.path.exists(BEST_MODEL_PATH):
raise Exception('File does not exist:', BEST_MODEL_PATH)
class SlimeVolleySelfPlayEnv(ObservationWrapperHalf):
# wrapper over the normal single player env, but loads the best self play model
def __init__(self):
super(SlimeVolleySelfPlayEnv,
self).__init__(slimevolleygym.SlimeVolleyEnv())
self.policy = self
save_freq = 1000
def mutate(length, mutation_rate, mutation_sigma):
# (not used, in case I wanted to do partial mutations)
# create an additive mutation vector of some size
mask = np.random.randint(int(1 / mutation_rate), size=length)
mask = 1 - np.minimum(mask, 1)
noise = np.random.normal(size=length) * mutation_sigma
return mask * noise
# Log results and get trained model location
from library import util
args = util.get_args(logdir='ga_selfplay_spike',
modelpath='../zoo/ga_sp/ga.json')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
GA_PATH = args.modelpath
# Create two instances of a feed forward policy we may need later.
policy_left = Model(mlp.games['slimevolleylite'])
policy_right = Model(mlp.games['slimevolleylite'])
param_count = policy_left.param_count
print("Number of parameters of the neural net policy:",
param_count) # 273 for slimevolleylite
# store our population here
population = np.random.normal(
from library.action_wrapper import ConstantNoiseActionWrapper
# Settings
SEED = 17
NUM_TIMESTEPS = int(2e6)
EVAL_FREQ = int(1e5)
EVAL_EPISODES = int(1e2)
BEST_THRESHOLD = 0.5 # must achieve a mean score above this to replace prev best self
RENDER_MODE = False # set this to false if you plan on running for full 1000 trials.
# Log results
from library import util
args = util.get_args('ppo_selfplay_constant',
'../zoo/ppo_sp/history_00000144.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
if not os.path.exists(BEST_MODEL_PATH):
raise Exception('File does not exist:', BEST_MODEL_PATH)
class SlimeVolleySelfPlayEnv(ConstantNoiseActionWrapper):
# wrapper over the normal single player env, but loads the best self play model
def __init__(self):
super(SlimeVolleySelfPlayEnv,
self).__init__(slimevolleygym.SlimeVolleyEnv())
self.policy = self
from shutil import copyfile # keep track of generations
# Settings
SEED = 17
NUM_TIMESTEPS = int(2e6)
EVAL_FREQ = int(1e5)
EVAL_EPISODES = int(1e2)
BEST_THRESHOLD = 0.5 # must achieve a mean score above this to replace prev best self
RENDER_MODE = False # set this to false if you plan on running for full 1000 trials.
# Log results
from library import util
from library import wrapper
args = util.get_args(logdir='ppo_selfplay_spike',
modelpath='../zoo/ppo_sp/history_00000144.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
if not os.path.exists(BEST_MODEL_PATH):
raise Exception('File does not exist:', BEST_MODEL_PATH)
class SlimeVolleySelfPlayEnv(wrapper.SpikeWrapper):
# wrapper over the normal single player env, but loads the best self play model
def __init__(self):
super(SlimeVolleySelfPlayEnv,
self).__init__(slimevolleygym.SlimeVolleyEnv())
self.policy = self
from stable_baselines.common import set_global_seeds
from stable_baselines.common.policies import MlpPolicy
from stable_baselines import bench, logger, PPO1
from stable_baselines.common.callbacks import EvalCallback
from library.action_wrapper import ConstantNoiseActionWrapper
NUM_TIMESTEPS = int(2e6)
SEED = 831
EVAL_FREQ = 200000
EVAL_EPISODES = 1000
# Log results
from library import util
args = util.get_args('ppo_constant', '../zoo/ppo/best_model.zip')
LOGDIR = args.logdir
if not os.path.exists(LOGDIR):
os.makedirs(LOGDIR)
BEST_MODEL_PATH = args.modelpath
if not os.path.exists(BEST_MODEL_PATH):
raise Exception('File does not exist:', BEST_MODEL_PATH)
def make_env(seed):
env = ConstantNoiseActionWrapper(gym.make("SlimeVolley-v0"))
env.seed(seed)
return env
