import graphs
import factored
N = 10000 # number of steps
DIM = 100 # number of joints
LIMIT = 150 # joint range = [-LIMIT, +LIMIT]
graphs.output_file('c3_fig3_19_demo_control_{}.html'.format(DIM))
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,
mesh_colors = [
'#E5E5E5'
] #[graphs.rgb2hex(graphs.rgba2rgb((255, 255, 255), (233, 78, 119, 0.5)))]
def pick(meshgrid, n=1):
assert n == 1
picks = []
for b in meshgrid.nonempty_bins:
c, s_signal, m_signal = b.draw()
picks.append((m_signal, s_signal))
return picks
# 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)
DIM = 20
RES = 20
N = 10000
# making graphs
graphs.output_file('c3_fig3_15_synergy.html')
timescale = [20000]
mesh_colors = ['#e5e5e5']
for ex_name in ['random.motor', 'random.goal']:
for synergy in [2, None]:
random.seed(0)
# instanciating the environment, and the Meshgrid
env_name, env_cfg = envs.kin(dim=DIM, limit=150, syn=synergy)
env = environments.Environment.create(env_cfg)
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)
mesh = explorers.ExplorerMeshGrid({'res': RES}, env.s_channels,
env.m_channels)
# running the exploration
prefix = '{} {}'.format(env_name, ex_name)
explorations, s_vectors, s_goals = factored.run_exploration(
env, ex, N, mesh=mesh, prefix=prefix)
import ddmab_ex
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]
from c3_graphlib import adapt_graphs
import fig4_4_adapt_ex
N = 5000
# making graphs
graphs.output_file('c4_figB_1_adapt_grid.html')
#for disturb in [0.001, 0.005, 0.025, 0.05, 0.1, 0.2, 0.5, 1.0]:
for disturb in [0.001, 0.05, 0.5]:
for res in [20, 40]:
random.seed(0)
# instanciating the environment
env_name, env_cfg = envs.kin(dim=20, limit=150)
env = environments.Environment.create(env_cfg)
# instanciating the explorer
ex_cfg = fig4_4_adapt_ex.grid_cfg._deepcopy()
ex_cfg.res = res
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, verbose=False)
# making graphs
# Licensed under the Open Science License (see http://fabien.benureau.com/openscience.html)
import random
import copy
import environments
import dotdot
import envs
import graphs
DIM = 20
RANGE = 150
# instanciating the two environments
env_name, env_cfg = envs.kin(dim=DIM, limit=RANGE)
env = environments.Environment.create(env_cfg)
lengths = [0.9**i for i in range(DIM)]
lengths = [s / sum(lengths) for s in lengths]
env2_name, env2_cfg = envs.kin(dim=DIM, limit=RANGE, lengths=lengths)
env2 = environments.Environment.create(env2_cfg)
# defining postures
random.seed(0)
m_channels = copy.deepcopy(env.m_channels)
for i in range(DIM - 1):
m_channels[i].bounds = (-45, 45)
m_signals = [environments.tools.random_signal(m_channels) for _ in range(50)]
# selecting some proeminent postures