for ex_name in ['random.goal']:
random.seed(0)
# instanciating the environment
env_name, env_cfg = envs.kin(dim=DIM, limit=LIMIT)
env = environments.Environment.create(env_cfg)
# instanciating the explorer
ex_cfg = exs.catalog[ex_name]._deepcopy()
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg)
# running exploration
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N, verbose=True)
# making graphs
for t1, t2 in [(0, 100), (100, N)]:
alpha = 1.0 if t2 == 100 else 0.25
graphs.posture_random(env, explorations[t1:t2], n=10,
alpha=0.75, radius_factor=0.35)
graphs.hold(True)
graphs.bokeh_spread(env.s_channels, s_vectors=s_vectors[:t2],
e_radius=1.5, e_alpha=alpha,
x_range=(-1.05, 1.05), y_range=(-1.05, 1.05),
title='{}::{}'.format(ex_name, env_name))
graphs.show()
ex_cfg = explorers.MetaExplorer.defcfg._deepcopy()
ex_cfg.eras = (MB, None)
ex_cfg.weights = ((1.0, 0.0), (0.0, 1.0))
ex_cfg.fallback = 0
ex_cfg.ex_0 = explorers.ReuseExplorer.defcfg._deepcopy()
ex_cfg.ex_0.reuse.res = RES
ex_cfg.ex_1 = explorers.RandomGoalExplorer.defcfg._deepcopy()
ex_cfg.ex_1.learner = exs.catalog['random.goal'].ex_1.learner._deepcopy()
# instanciating the environment and the Explorer
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg, datasets=[dataset])
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N)
graphs.output_file('c5_fig5_2.3.4_reuse.html')
# Graph of reused effects in first env
s_vectors0 = []
for explo in explorations[:MB]:
feedback = env0.execute(explo[0]['m_signal'])
s_vectors0.append(
explorers.tools.to_vector(feedback['s_signal'], env0.s_channels))
graphs.spread(env.s_channels,
s_vectors=s_vectors0[:MB],
e_radius=3.0,
e_alpha=1.0,
e_color='#DF6464',
# instanciating the environment, and the Meshgrid
env_name, env_cfg = envs.kin(dim=DIM, limit=150)
env = environments.Environment.create(env_cfg)
ex_cfg = exs.catalog['random.goal']._deepcopy()
ex_cfg.eras = (MB, None)
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg)
mesh = explorers.ExplorerMeshGrid({'res': RES}, env.s_channels, env.m_channels)
# running the exploration
explorations, s_vectors, s_goals = factored.run_exploration(env,
ex,
N,
mesh=mesh)
mesh_picks = pick(mesh)
# Spread Graph
for t in [MB, 200, 400, N]:
alpha = 1.0 if t != N else 0.5
graphs.spread(mesh.s_channels,
s_vectors=s_vectors[:t],
grid=False,
e_radius=1.5,
e_alpha=alpha,
title='first arm - {} steps'.format(t))
# graphs.hold(True)
# if t == N:
# graphs.bokeh_spread(mesh.s_channels, s_vectors=[tools.to_vector(s, mesh.s_channels) for m, s in mesh_picks],
DIM = 20
N = 5000
# preparing graphs
graphs.output_file('ddmab.html')
for disturb in [0.001, 0.002, 0.05, 0.5]:
random.seed(0)
# instanciating the environment
env_name, env_cfg = envs.kin(dim=DIM, limit=150)
env = environments.Environment.create(env_cfg)
# instanciating the explorer
ex_cfg = ddmab_ex.ex_cfg._deepcopy()
ex_cfg.ex_1.learner.m_disturb = disturb
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg)
# running the exploration
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N)
# making graphs
for i, weights in enumerate(ex.weight_history['data']):
ex.weight_history['data'][i] = [1000*w for w in weights]
graphs.adapt_graphs(ex.cfg, explorations, s_vectors, ex.weight_history, title='{}'.format(disturb))
graphs.show()
