def main(args):
if args.environment == 1:
from envs.continuous_1 import ContinuousEnv1 as Env
elif args.environment == 2:
from envs.continuous_2 import ContinuousEnv2 as Env
else:
from envs.continuous_3 import ContinuousEnv3 as Env
env = Env()
g = model_utils.BalancedMLP([1], [8, 16, 32], 0.0001, 128, 0.0001, verbose=True)
def visualize_b(state_action_partition):
vis_utils.plot_background(env, show=False)
xx, yy = np.meshgrid(np.arange(0, env.STATE_ACTION_MAP.shape[1], 0.01),
np.arange(0, env.STATE_ACTION_MAP.shape[0], 0.01))
data = np.c_[xx.ravel(), yy.ravel()]
Z = g.batch_predict(data[:, 0], data[:, 1])
Z = np.array(Z).reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.4)
vis_utils.plot_state_action_partition(state_action_partition, show=True)
experience = gather_experience(env, args.num_experience)
h**o = OnlineHomomorphismGDict(experience, g, sample_actions, args.threshold_multiplier, args.threshold_minimum,
OnlineHomomorphismGDict.RESOLVE_IGNORE, 20, visualize_b=visualize_b)
h**o.partition_iteration()
def main(args):
env = ContinuousEnv3()
g = model_utils.BalancedMLP([1], [8, 16], 0.001, 32, 0.0, verbose=True)
def visualize_b(state_action_partition):
vis_utils.plot_background(env, show=False)
vis_utils.plot_decision_boundary(g,
env.STATE_ACTION_MAP.shape[1],
env.STATE_ACTION_MAP.shape[0],
show=False)
vis_utils.plot_state_action_partition(state_action_partition,
show=True)
def visualize_sb(state_partition):
vis_utils.plot_background(env, show=False)
vis_utils.plot_state_partition(state_partition, show=True)
experience = gather_experience(env, args.num_experience)
h**o = online_homomorphism_g.OnlineHomomorphismG(
experience,
g,
sample_actions,
args.state_action_slit_threshold,
args.state_split_threshold,
20,
visualize_b=visualize_b,
visualize_sb=visualize_sb)
h**o.partition_iteration()
hits, total = evaluation.overlap(env, list(h**o.partition))
print("{:.2f}% accuracy ({:d}/{:d})".format((hits / total) * 100, hits,
total))
def main(args):
env = ContinuousEnv3()
g = model_utils.BalancedMLP([1], [8, 16, 32], 0.0001, 128, 0.0001, verbose=True)
def visualize_b(state_action_partition):
vis_utils.plot_background(env, show=False)
xx, yy = np.meshgrid(np.arange(0, env.STATE_ACTION_MAP.shape[1], 0.01),
np.arange(0, env.STATE_ACTION_MAP.shape[0], 0.01))
data = np.c_[xx.ravel(), yy.ravel()]
Z = g.batch_predict(data[:, 0], data[:, 1])
Z = np.array(Z).reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.4)
vis_utils.plot_state_action_partition(state_action_partition, show=True)
def visualize_sb(state_partition):
vis_utils.plot_background(env, show=False)
vis_utils.plot_state_partition(state_partition, show=True)
experience = gather_experience(env, args.num_experience)
h**o = online_homomorphism_g.OnlineHomomorphismG(experience, g, sample_actions, 1, 1, 20, visualize_b=visualize_b,
visualize_sb=visualize_sb)
h**o.partition_iteration()
def run(resolution):
env = ContinuousEnv3()
g = model_utils.BalancedMLP([1], [8, 16], 0.001, 32, 0.0, verbose=True)
experience = gather_experience(env, NUM_EXPERIENCE)
h**o = OnlineHomomorphismGDict(experience, g, sample_actions,
SPLIT_THRESHOLD, resolution, 20)
h**o.partition_iteration()
hits, total = evaluation.overlap(env, list(h**o.partition))
accuracy = hits / total
return accuracy
def run(num_experience, split_threshold, min_confidence):
env = ContinuousEnv3()
g = model_utils.BalancedMLP([1], [8, 16], 0.001, 32, 0.0, verbose=True)
experience = gather_experience(env, num_experience)
h**o = OnlineHomomorphismG(experience, g, sample_actions, split_threshold,
0, 20, min_confidence)
h**o.partition_iteration()
hits, total = evaluation.overlap(env, list(h**o.partition))
accuracy = hits / total
return accuracy
def run(num_experience, threshold_multiplier):
env = ContinuousEnv3()
g = model_utils.BalancedMLP([1], [8, 16], 0.001, 32, 0.0, verbose=True)
experience = gather_experience(env, num_experience)
h**o = OnlineHomomorphismGDict(experience, g, sample_actions, threshold_multiplier, MINIMUM_THRESHOLD,
OnlineHomomorphismGDict.RESOLVE_IGNORE, 20)
h**o.partition_iteration()
hits, total = evaluation.overlap(env, list(h**o.partition))
accuracy = hits / total
return accuracy
def main(args):
if args.environment == 1:
from envs.continuous_1 import ContinuousEnv1 as Env
elif args.environment == 2:
from envs.continuous_2 import ContinuousEnv2 as Env
else:
from envs.continuous_3 import ContinuousEnv3 as Env
env = Env()
g = model_utils.BalancedMLP([1], [8, 16, 32], 0.0001, 128, 0.0001, verbose=True)
def visualize_b(state_action_partition):
vis_utils.plot_background(env, show=False)
xx, yy = np.meshgrid(np.arange(0, env.STATE_ACTION_MAP.shape[1], 0.01),
np.arange(0, env.STATE_ACTION_MAP.shape[0], 0.01))
data = np.c_[xx.ravel(), yy.ravel()]
Z = g.batch_predict(data[:, 0], data[:, 1])
Z = np.array(Z).reshape(xx.shape)
plt.contourf(xx, yy, Z, alpha=0.4)
vis_utils.plot_state_action_partition(state_action_partition, show=True)
def visualize_ignored(states):
vis_utils.plot_background(env, show=False)
plt.hist(states, bins=10, range=[0, len(env.STATE_MAP)], normed=True)
plt.show()
experience = gather_experience(env, args.num_experience)
h**o = OnlineHomomorphismGDict(experience, g, sample_actions, args.b_threshold, args.conf_threshold,
OnlineHomomorphismGDict.RESOLVE_ADD_CLOSEST, 20, percentile=args.percentile,
visualize_b=visualize_b, visualize_conf=vis_utils.show_confidences,
visualize_ignored=visualize_ignored)
h**o.partition_iteration()