Esempi in Python per filter_by_flat_params

Esempio n. 1

File: pusher_reward_type_ablation.py Progetto: Asap7772/rail-rl-franka-eval

import matplotlib
from visualization.grill.config import (
    output_dir,
    ashvin_base_dir,
    format_func,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from railrl.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/steven/pushing-multipushing/pusher-reward-variants'
],
                      f,
                      suppress_output=True)
plot.tag_exps(exps, "name", "dsae")

plot.comparison(exps,
                ["Final  puck_distance Mean", "Final  hand_distance Mean"],
                figsize=(6, 4),
                vary=["vae_wrapped_env_kwargs.reward_params.type"],
                default_vary={"reward_params.type": "unknown"},
                smooth=plot.padded_ma_filter(10),
                xlim=(0, 250000),
                ylim=(0.15, 0.22),
                method_order=None)

Esempio n. 2

File: pusher_relabeling_ablation.py Progetto: Asap7772/rail-rl-franka-eval

)
import matplotlib.pyplot as plt
from railrl.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, 250000),
    ylim=(0.14, 0.24),

Esempio n. 3

File: reacher_baselines.py Progetto: Asap7772/rail-rl-franka-eval

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 railrl.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

]
f = plot.exclude_by_flat_params({'grill_variant.vae_path': '/home/khazatsky/rail/data/rail-khazatsky/sasha/PCVAE/standard/run1002/id0/vae.pkl',})
CVAE = plot.load_exps(dirs, f, suppress_output=True)
plot.tag_exps(CVAE, "name", "CVAE")

dirs = [
    '/home/khazatsky/rail/data/rail-khazatsky/sasha/cond-rig/pointmass/dynamics/run12',
       ]
CDVAE = plot.load_exps(dirs, suppress_output=True)
plot.tag_exps(CDVAE, "name", "Dynamics CVAE")


dirs = [
    '/home/khazatsky/rail/data/rail-khazatsky/sasha/cond-rig/pointmass/adversarial/run10/',
       ]
f = plot.filter_by_flat_params({'grill_variant.vae_path': '/home/khazatsky/rail/data/rail-khazatsky/sasha/PCVAE/ACE/run1000/id1/vae.pkl',})
ACE = plot.load_exps(dirs, f, suppress_output=True)
plot.tag_exps(ACE, "name", "Adversarially Conditioned VAE")

dirs = [
    '/home/khazatsky/rail/data/rail-khazatsky/sasha/cond-rig/pointmass/CADVAE/run10/',
       ]

CADVAE = plot.load_exps(dirs, suppress_output=True)
plot.tag_exps(CADVAE, "name", "Adversarially Conditioned Dynamics VAE")

dirs = [
    '/home/khazatsky/rail/data/rail-khazatsky/sasha/cond-rig/pointmass/standard/',
]
f = plot.filter_by_flat_params({'grill_variant.vae_path': '/home/khazatsky/rail/data/rail-khazatsky/sasha/PCVAE/standard/run1001/id0/vae.pkl',})
DCVAE = plot.load_exps(dirs, f, suppress_output=True)

Esempio n. 5

    output_dir,
    ashvin_base_dir,
    format_func,
    our_method_name,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from railrl.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

File: pusher_baselines.py Progetto: Asap7772/rail-rl-franka-eval

    vitchyr_base_dir,
    format_func,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from railrl.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. 7

    our_method_name,
    configure_matplotlib,
)
import matplotlib.pyplot as plt
from railrl.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':

Esempio n. 8

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

configure_matplotlib(matplotlib)


f = plot.filter_by_flat_params({
    'grill_variant.algo_kwargs.rl_offpolicy_num_training_steps': 10000,
})
ours = plot.load_exps([
  ashvin_base_dir + "s3doodad/ashvin/corl2019/offpolicy/rig-dcvae-offpolicy2-sweep1/run3",
  ashvin_base_dir + "s3doodad/ashvin/corl2019/offpolicy/rig-dcvae-offpolicy2-sweep1/run4",
  ashvin_base_dir + "s3doodad/ashvin/corl2019/offpolicy/rig-dcvae-offpolicy2-sweep1/run5",
  ], f, suppress_output=True)
plot.tag_exps(ours, "name", "off-policy")


baseline = plot.load_exps([ashvin_base_dir + "s3doodad/ashvin/corl2019/offpolicy/rig-dcvae-offpolicy2-baseline", ], suppress_output=True)
plot.tag_exps(baseline, "name", "on-policy")

plot.comparison(
    ours + baseline,
    "evaluation/distance_to_target Mean",
    vary=["name"],
    # xlabel="evaluation/num steps total",