def run_experiment(Lambda,
alpha,
twe,
trunc_normal,
subspaces,
num_runs,
num_episodes=20000,
num_procs=None,
name=""):
returns = np.empty((num_runs, num_episodes), dtype=np.float64)
results.append(returns)
for i in xrange(num_runs):
print name
agent = PolicyGradientAgent(simulator,
Lambda=Lambda,
alpha_u=alpha,
alpha_v=alpha,
tile_weight_exponent=twe,
trunc_normal=trunc_normal,
subspaces=subspaces)
agent.persist_state()
framework = Framework(simulator, agent, num_episodes=num_episodes)
framework.train(num_procs=num_procs)
returns[i] = framework.returns
random = np.random.randint(sys.maxsize)
directory = 'data/%s/' % (name)
filename = directory + ('%d.npy' % (random))
try:
os.makedirs(directory)
except OSError:
pass
np.save(filename, returns)
return returns
def run_experiment(Lambda, alpha, twe, trunc_normal, subspaces, num_runs,num_episodes=20000, num_procs=None,name=""):
returns = np.empty((num_runs, num_episodes), dtype=np.float64)
results.append(returns)
for i in xrange(num_runs):
print(name)
agent = PolicyGradientAgent (simulator,
Lambda=Lambda, alpha_u=alpha, alpha_v=alpha,
tile_weight_exponent=twe,
trunc_normal=trunc_normal,
subspaces=subspaces)
agent.persist_state()
framework = Framework(simulator, agent, num_episodes=num_episodes)
framework.train(num_procs=num_procs)
returns[i] = framework.returns
random = np.random.randint(sys.maxsize)
directory = 'data/%s/' % (name)
filename = directory + ('%d.npy' % (random))
try:
os.makedirs(directory)
except OSError:
pass
np.save (filename, returns)
return returns
import os
import gym
import numpy as np
from PolicyGradientAgent import PolicyGradientAgent
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
env = gym.make('LunarLander-v2')
action_size = env.action_space.n
state_size = 8
agent = PolicyGradientAgent(state_size, action_size)
print("Training...")
train_episodes = 5000
avg_score = 0
loss = 0
for episode in range(train_episodes):
state = env.reset()
state = np.reshape(state, [1, state_size])
cum_reward = 0
for i in range(1000):
action = agent.act(state, is_training=True)
next_state, reward, done, _ = env.step(action)
next_state = np.reshape(next_state, [1, state_size])
print(env.observation_space.shape, env.action_space.n)
env.reset()
test_mode = False
num_games = 2500
best_score = -np.inf
scores = []
eps_history = []
state_dims = env.observation_space.shape[0]
num_actions = env.action_space.n
lr = 0.001
gamma = 0.99
agent = PolicyGradientAgent(lr=lr,
gamma=gamma,
state_dims=state_dims,
num_actions=num_actions,
env_name='lunar_lander',
checkpoint_dir='temp/')
if test_mode:
agent.load_model()
# env = gym.wrappers.Monitor(env, 'temp/lunar_lander',
# video_callable=lambda episode_id: True, force=True)
for count in range(num_games):
state = env.reset()
done = False
score = 0
while not done:
env.render()
def make_framework(Lambda):
agent = PolicyGradientAgent(simulator, Lambda=Lambda)
filename = "data/saved_state_lambda" + str(Lambda) + ".npy"
agent.save_state(filename)
agent.load_state(filename, mmap_mode="r+")
return Framework(simulator, agent)
def make_framework(Lambda):
agent = PolicyGradientAgent(simulator, Lambda=Lambda)
filename = 'data/saved_state_lambda' + str(Lambda) + '.npy'
agent.save_state(filename)
agent.load_state(filename, mmap_mode='r+')
return Framework(simulator, agent)
