Esempi in Python per filter_by_flat_params

Esempio n. 1

    vitchyr_base_dir,
    format_func,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

dirs = [
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/state-dense-wider2/run1',
]
f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    'replay_kwargs.fraction_goals_are_env_goals':
    0.0
})
oracle = plot.load_exps(dirs, f, suppress_output=True)
plot.tag_exps(oracle, "name", "oracle")

dirs = [
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-wider3/run1',
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-wider3-relabeling/run1',
]
f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    "replay_kwargs.fraction_goals_are_env_goals":

Esempio n. 2

)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

dirs = [
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-wider3/run1',
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-wider3-relabeling/run1',
]
f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    "replay_kwargs.fraction_goals_are_env_goals":
    0.5,
    "replay_kwargs.fraction_goals_are_rollout_goals":
    0.2
})
exps = plot.load_exps(dirs, suppress_output=True)
plot.comparison(
    exps,
    ["Final  puck_distance Mean", "Final  hand_distance Mean"],
    vary=[
        "replay_kwargs.fraction_goals_are_env_goals",
        "replay_kwargs.fraction_goals_are_rollout_goals"
    ],
    default_vary={"replay_strategy": "future"},
    smooth=plot.padded_ma_filter(10),
    xlim=(0, 500000),
    ylim=(0.14, 0.26),

Esempio n. 3

import matplotlib
from visualization.grill.config import (
    output_dir,
    ashvin_base_dir,
    vitchyr_base_dir,
    format_func,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    'replay_kwargs.fraction_goals_are_env_goals':
    0.5
})

oracle = plot.load_exps(
    [ashvin_base_dir + "s3doodad/share/reacher/reacher-baseline-oracle"],
    suppress_output=True)
plot.tag_exps(oracle, "name", "oracle")

ours = plot.load_exps(
    [ashvin_base_dir + "s3doodad/share/reacher/reacher-main-results-ours"],
    suppress_output=True)
plot.tag_exps(ours, "name", "ours")
f = plot.filter_by_flat_params({
    'replay_kwargs.fraction_goals_are_env_goals': 0.0,
    'reward_params.type': 'latent_distance'

Esempio n. 4

import matplotlib
from visualization.grill.config import (
    output_dir,
    ashvin_base_dir,
    format_func,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

f = plot.filter_by_flat_params(
    {'replay_kwargs.fraction_goals_are_env_goals': 0.5})
exps = plot.load_exps(
    [ashvin_base_dir + "s3doodad/share/reward-reaching-sweep"],
    f,
    suppress_output=True)

plot.comparison(
    exps,
    "Final  distance Mean",
    vary=["reward_params.type"],
    # smooth=plot.padded_ma_filter(10),
    ylim=(0.0, 0.2),
    xlim=(0, 10000),
    # method_order=[1, 0, 2]),
    figsize=(6, 4),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")

Esempio n. 5

    output_dir,
    ashvin_base_dir,
    format_func,
    our_method_name,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

dirs = [
    ashvin_base_dir + 's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-multi3/run1',
]
f = plot.filter_by_flat_params({
                                   'algo_kwargs.num_updates_per_env_step': 4,
                                   "replay_kwargs.fraction_goals_are_env_goals": 0.5
                               })
ours = plot.load_exps(dirs, f, suppress_output=True)
plot.tag_exps(ours, "name", "ours")

dirs = [
    ashvin_base_dir + 's3doodad/ashvin/vae/fixed3/sawyer-pusher/state-dense-multi1/run1',
]
f = plot.filter_by_flat_params({
                                   'replay_kwargs.fraction_goals_are_env_goals': 0.5,
                                   'algo_kwargs.reward_scale': 1e-4
                               })
oracle = plot.load_exps(dirs, f, suppress_output=True)
plot.tag_exps(oracle, "name", "oracle")

# dsae = plot.load_exps(

Esempio n. 6

    our_method_name,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from rlkit.visualization import plot_util as plot

configure_matplotlib(matplotlib)

dirs = [
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-multi3/run1',
]
f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    'rdim':
    16,
    'replay_kwargs.fraction_goals_are_rollout_goals':
    0.2
})
her = plot.load_exps(dirs, f, suppress_output=True)

dirs = [
    ashvin_base_dir +
    's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-multi3/run1',
]
f = plot.filter_by_flat_params({
    'algo_kwargs.num_updates_per_env_step':
    4,
    'rdim':
    16,
    'replay_kwargs.fraction_goals_are_rollout_goals':