lr = plot.load_exps(
[vitchyr_base_dir + "papers/nips2018/06-06-lr-baseline-pusher-0.2-range/"],
plot.filter_by_flat_params({
'rdim': '16--lr-1e-3',
}),
suppress_output=True,
)
plot.tag_exps(lr, "name", "l&r")
plot.comparison(
#lr + ours + oracle + her + dsae,
ours + oracle + her + lr + dsae,
["Final puck_distance Mean", "Final hand_distance Mean"],
vary=["name"],
smooth=plot.padded_ma_filter(10),
#method_order=[4, 0, 1, 3, 2],
ylim=(0.1, 0.28),
# xlim=(0, 250000),
xlim=(0, 500000),
figsize=(6, 4),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("") # "Final Distance to Goal")
plt.title("Visual Pusher Baselines")
plt.legend([]) # [our_method_name, "DSAE", "HER", "Oracle", "L&R", ])
plt.tight_layout()
plt.savefig(output_dir + "pusher_baselines.pdf")
print("File saved to", output_dir + "pusher_baselines.pdf")
configure_matplotlib(matplotlib)
dirs = [
ashvin_base_dir + 's3doodad/share/camera_ready_door',
]
exps = plot.load_exps(dirs, suppress_output=True)
plot.comparison(
exps,
["Final angle_difference Mean"],
[
# "seed",
"exp_prefix",
],
default_vary={"env_kwargs.randomize_position_on_reset": True},
smooth=plot.padded_ma_filter(10),
print_final=False,
print_min=False,
print_plot=True,
xlim=(0, 200000),
# ylim=(0, 0.35),
figsize=(6, 4),
method_order=(3, 2, 4, 0, 1),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("Final Distance to Goal")
plt.legend([])
# [],
# [
# "RIG",
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),
figsize=(6, 5),
method_order=[2, 1, 0, 3],
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
# plt.ylabel("")
plt.xlabel("Timesteps")
plt.ylabel("Final Distance to Goal")
plt.title("Visual Pusher")
plt.legend(
[
our_method_name,
"None",
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")
plt.ylabel("Final Distance to Goal")
plt.title("Visual Reacher")
plt.legend([])
plt.tight_layout()
plt.savefig(output_dir + "reacher_reward_type_ablation.pdf")
print("File saved to", output_dir + "reacher_reward_type_ablation.pdf")
dsae = plot.load_exps(
[ashvin_base_dir + 's3doodad/share/steven/no-relabeling-test'],
suppress_output=True)
plot.tag_exps(dsae, "name", "dsae")
lr = plot.load_exps([
vitchyr_base_dir +
"papers/nips2018/autoencoder_result/05-25-sawyer-reacher-autoencoder-ablation-final/"
],
suppress_output=True)
plot.tag_exps(lr, "name", "l&r")
plot.comparison(
ours + oracle + her + lr + dsae,
"Final distance Mean",
vary=["name"],
figsize=(6, 4),
# figsize=(7.5, 4),
method_order=[4, 0, 1, 3, 2],
ylim=(0.0, 0.25),
xlim=(0, 10000),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("Final Distance to Goal")
plt.title("Visual Reacher Baselines")
plt.legend([])
# plt.tight_layout()
# plt.savefig(output_dir + "reacher_baselines.pdf")
# print("File saved to", output_dir + "reacher_baselines.pdf")
# plt.legend(
from rlkit.visualization import plot_util as plot
configure_matplotlib(matplotlib)
exps = plot.load_exps([
ashvin_base_dir +
"s3doodad/share/reacher/reacher-abalation-resample-strategy"
],
suppress_output=True)
# plot.tag_exps(exps, "name", "oracle")
plot.comparison(
exps,
"Final distance Mean",
vary=[
"replay_kwargs.fraction_goals_are_env_goals",
"replay_kwargs.fraction_goals_are_rollout_goals"
],
# smooth=plot.padded_ma_filter(10),
ylim=(0.04, 0.2),
xlim=(0, 10000),
method_order=[2, 1, 0, 3],
figsize=(6, 4))
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("Final Distance to Goal")
plt.title("Visual Reacher")
# plt.legend([]) # ["Ours", "No Relabeling", "HER", "VAE Only", ])
plt.legend([])
plt.tight_layout()
plt.savefig(output_dir + "reacher_relabeling_ablation.pdf")
print("File saved to", output_dir + "reacher_relabeling_ablation.pdf")
plot.tag_exps(ours, "name", "ours")
# f = plot.filter_by_flat_params({'algorithm': 'Sparse-HER', 'reward_params.epsilon': 0.1})
her = plot.load_exps([ashvin_base_dir + "s3doodad/share/real-reacher/her"],
suppress_output=True)
plot.tag_exps(her, "name", "her")
# f = plot.filter_by_flat_params({'algorithm': 'TDM'})
oracle = plot.load_exps(
[ashvin_base_dir + "s3doodad/share/real-reacher/oracle"],
suppress_output=True)
plot.tag_exps(oracle, "name", "oracle")
plot.comparison(
ours + her + oracle,
"Test Final End Effector Distance from Target Mean",
vary=["name"],
ylim=(0.0, 0.35),
xlim=(0, 10000),
method_order=[2, 0, 1],
figsize=(6, 5),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.ylabel("Final Distance to Goal")
plt.title("Real-World Visual Reacher")
leg = plt.legend(
[our_method_name, "HER", "Oracle"],
bbox_to_anchor=(0.49, -0.2),
loc="upper center",
ncol=4,
handlelength=1,
)
leg.get_frame().set_alpha(0.9)
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")
plot.comparison(
ours + logprob + exps,
"Final total_distance Mean",
figsize=(7.5, 4),
vary=["name"],
default_vary={"reward_params.type": "unknown"},
smooth=plot.padded_ma_filter(10),
xlim=(0, 250000),
method_order=[1, 0, 2],
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("")
plt.title("Visual Multi-object Pusher")
plt.legend([our_method_name, "Log Prob.", "Pixel MSE", ],
bbox_to_anchor=(1.0, 0.5), loc="center left",)
plt.tight_layout()
plt.savefig(output_dir + "multiobj_pusher_reward_type_ablation.pdf")
print("File saved to", output_dir + "multiobj_pusher_reward_type_ablation.pdf")
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/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)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("")
plt.title("Visual Pusher")
plt.legend([])
plt.tight_layout()
plt.savefig(output_dir + "pusher_reward_type_ablation.pdf")
configure_matplotlib(matplotlib)
dirs = [
ashvin_base_dir + 's3doodad/share/camera_ready_pick',
]
pick_exps = plot.load_exps(dirs, suppress_output=True)
plot.comparison(
pick_exps,
["Final hand_and_obj_distance Mean"],
[
# "seed",
"exp_prefix",
"train_vae_variant.vae_type",
],
default_vary={"train_vae_variant.vae_type": True},
smooth=plot.padded_ma_filter(10),
print_final=False,
print_min=False,
print_plot=True,
xlim=(0, 500000),
# ylim=(0, 0.35),
figsize=(7.5, 4),
method_order=(2, 1, 3, 4, 0),
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.xlabel("Timesteps")
plt.ylabel("")
# plt.legend()
plt.legend(
[
"RIG",
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
label_to_color = {
'ours': colors[0],
'oracle': colors[3],
'her': colors[1],
'dsae': colors[2],
'l&r': colors[4],
}
plot.comparison(
ours + oracle + dsae + lr + her,
["Final total_distance Mean"],
vary=["name"],
# default_vary={"replay_strategy": "future"},
smooth=plot.padded_ma_filter(10),
# xlim=(0, 250000),
xlim=(0, 500000),
ylim=(0.15, 0.4),
# figsize=(7, 3.5),
figsize=(7.5, 4),
method_order=[4, 0, 1, 3, 2],
# label_to_color=label_to_color,
)
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
# leg.get_frame().set_alpha(0.9)
plt.xlabel("Timesteps")
plt.ylabel("") # "Final Distance to Goal")
plt.title("Visual Multi-object Pusher Baselines")
plt.legend(
[
our_method_name,
"DSAE",
0.0
})
norelabel = plot.load_exps(dirs, f, suppress_output=True)
dirs = [
ashvin_base_dir +
's3doodad/ashvin/vae/fixed3/sawyer-pusher/vae-dense-multi3-fullrelabel/run1',
]
fullrelabel = plot.load_exps(dirs, suppress_output=True)
plot.comparison(
her + fullrelabel + norelabel,
"Final total_distance Mean",
[
"replay_kwargs.fraction_goals_are_rollout_goals",
"replay_kwargs.fraction_goals_are_env_goals",
],
# ["training_mode", "replay_kwargs.fraction_goals_are_env_goals", "replay_kwargs.fraction_goals_are_rollout_goals", "rdim"],
default_vary={"replay_strategy": "future"},
smooth=plot.padded_ma_filter(10),
figsize=(7.5, 4),
xlim=(0, 500000),
ylim=(0.15, 0.35),
method_order=[1, 2, 0, 3])
plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(format_func))
plt.ylabel("")
plt.xlabel("Timesteps")
plt.title("Visual Multi-object Pusher")
leg = plt.legend(
[
our_method_name,
"None",
"Future",
