def main():
tf.Session().__enter__()
seed = 1
np.random.seed(seed)
tf.set_random_seed(seed)
# disable logging during testing
if test_model:
log_interval = int(1e20)
else:
log_interval = 1
# env = gym.make('CartPole-v0')
# Create Multi-Snake environment
spacing = 22
grid_dim = 10
history = 4
env = MultiSnake(num_agents=num_agents, num_fruits=3, spacing=spacing, grid_dim=grid_dim, flatten_states=False,
reward_killed=-1.0,history=4,save_gif=save_gif)
env.reset()
env = makegymwrapper(env, visualize=test_model)
ppo2_actprior.learn(policy=MultiSnakeCNNPolicy, env=env, nsteps=buf_size, nminibatches=nminibatches,
lam=lam, gamma=gamma, noptepochs=4, log_interval=log_interval,
ent_coef=.01,
lr=1e-3,
cliprange=.2,
total_timesteps=int(1e20),
callback_fn=on_update)
giffn = trainhistdir + 'video.gif'
# handle command line arguments
test_model, save_gif = handle_args(test_model, save_gif)
if save_gif:
num_episodes = 3
# Create custom OpenAIgym environment
num_agents = 1
spacing = 22
grid_dim = 10
e = MultiSnake(num_agents=num_agents,
num_fruits=3,
spacing=spacing,
grid_dim=grid_dim,
flatten_states=False,
reward_killed=-1.0,
save_gif=save_gif)
env = OpenAIGym_custom(e, "MultiSnake", visualize=test_model)
network_spec = [
dict(type='conv2d', size=16, window=3, stride=1, bias=True),
dict(type='conv2d', size=32, window=3, stride=1, bias=True),
dict(type='flatten'),
dict(type='dense', size=256, bias=True)
]
states_preprocessing = [
# dict(type='divide',scale=2)
episode_offset = 0
max_episode_timesteps = 1000
giffn = trainhistdir + 'video.gif'
# handle command line arguments
test_model, save_gif = handle_args(test_model, save_gif)
if save_gif:
num_episodes = 3
# Create custom OpenAIgym environment
num_agents = 1
spacing = 22
grid_dim = 10
e = MultiSnake(num_agents=num_agents, num_fruits=3, spacing=spacing, grid_dim=grid_dim, save_gif=save_gif)
env = OpenAIGym_custom(e, "MultiSnake", visualize=test_model)
# Network as list of layers
network_spec = [
dict(type='dense', size=128, activation='tanh'),
dict(type='dense', size=128, activation='tanh')
]
agent = PPOAgent(
states=env.states,
actions=env.actions,
network=network_spec,
update_mode = dict(
unit='episodes',