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plotting_utils.py
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import numpy as np
# colors = {
# "sac_recovery_pets": "red",
# "sac_recovery_ddpg": "blue",
# "sac_penalty": "green",
# "sac_lagrangian": "black",
# "sac_rcpo": "purple",
# "sac_rspo": "orange",
# "sac_vanilla": "olive",
# "sac_sqrl": "magenta",
# "sac_recovery_disable_relabel": "blue",
# "sac_recovery_pets_1k": "green",
# "sac_recovery_pets_5k": "purple"
# }
colors = {
"sac_vanilla": "#AA5D1F",
"sac_lagrangian": "#BA2DC1",
"sac_rspo": "#6C2896",
"sac_sqrl": "#D43827",
"sac_penalty": "#4899C5",
"sac_rcpo": "#34539C",
"sac_recovery_ddpg": "red",
"sac_recovery_pets": "#349C26",
"sac_recovery_pets_100": "#AA5D1F",
"sac_recovery_pets_500": "#34539C",
"sac_recovery_pets_1k": "#4899C5",
"sac_recovery_pets_5k": "#D43827",
"sac_recovery_pets_20k": "#349C26",
"sac_recovery_pets_100": "#AA5D1F",
"sac_recovery_pets_500": "#34539C",
"sac_recovery_pets_1k": "#4899C5",
"sac_recovery_pets_5k": "#D43827",
"sac_recovery_pets_20k": "#349C26",
"reward_5": "#AA5D1F",
"reward_10": "#34539C",
"reward_15": "#4899C5",
"reward_25": "#D43827",
"reward_50": "#349C26",
"nu_5": "#AA5D1F",
"nu_10": "#34539C",
"nu_15": "#4899C5",
"nu_25": "#D43827",
"nu_50": "#349C26",
"lambda_5": "#AA5D1F",
"lambda_10": "#34539C",
"lambda_15": "#4899C5",
"lambda_25": "#D43827",
"lambda_50": "#349C26",
"eps_0.15": "#AA5D1F",
"eps_0.25": "#34539C",
"eps_0.35": "#4899C5",
"eps_0.45": "#D43827",
"eps_0.55": "#349C26",
"sac_recovery_pets_ablations": "#349C26",
"sac_recovery_pets_disable_relabel": "#34539C",
"sac_recovery_pets_disable_offline": "#AA5D1F",
"sac_recovery_pets_disable_online": "#D43827",
"multitask": "#AA5D1F",
"meta": "#BA2DC1",
}
# colors = {
# "sac_recovery_pets": (0, 0.45, 0.7),
# "sac_recovery_ddpg": (0.8, 0.6, 0.7),
# "sac_penalty": (0, 0.6, 0.5),
# "sac_lagrangian": "black",
# "sac_rcpo": (0.8, 0.4, 0),
# "sac_rspo": (0.9, 0.6, 0),
# "sac_vanilla": (0.35, 0.7, 0.9),
# "sac_sqrl": (0.2, 0.7, 0.3)
# }
names = {
"sac_recovery_pets": "SAC + Model-Based Recovery",
"sac_recovery_pets_ablations": "Ours: Recovery RL (MB Recovery)",
"sac_recovery_ddpg": "SAC + Model-Free Recovery",
"sac_penalty": "SAC + Reward Penalty (RCPO)",
"sac_lagrangian": "SAC + Lagrangian",
"sac_rcpo": "SAC + Critic Penalty (RCPO)",
"sac_rspo": "SAC + RSPO",
"sac_vanilla": "SAC",
"sac_sqrl": "SQRL",
"sac_recovery_pets_100": "100",
"sac_recovery_pets_500": "500",
"sac_recovery_pets_1k": "1K",
"sac_recovery_pets_5k": "5K",
"sac_recovery_pets_20k": "20K",
"reward_5": "$\lambda = 5$",
"reward_10": "$\lambda = 10$",
"reward_15": "$\lambda = 15$",
"reward_25": "$\lambda = 25$",
"reward_50": "$\lambda = 50$",
"nu_5": "$\lambda = 5$",
"nu_10": "$\lambda = 10$",
"nu_15": "$\lambda = 15$",
"nu_25": "$\lambda = 25$",
"nu_50": "$\lambda = 50$",
"lambda_5": "$\lambda = 5$",
"lambda_10": "$\lambda = 10$",
"lambda_15": "$\lambda = 15$",
"lambda_25": "$\lambda = 25$",
"lambda_50": "$\lambda = 50$",
"eps_0.15": "$\epsilon_{risk} = 0.15$",
"eps_0.25": "$\epsilon_{risk} = 0.25$",
"eps_0.35": "$\epsilon_{risk} = 0.35$",
"eps_0.45": "$\epsilon_{risk} = 0.45$",
"eps_0.55": "$\epsilon_{risk} = 0.55$",
"sac_recovery_pets_disable_relabel": "Ours - Action Relabeling",
"sac_recovery_pets_disable_offline": "Ours - Offline Training",
"sac_recovery_pets_disable_online": "Ours - Online Training",
"multitask": "Multitask",
"meta": "Metalearning",
}
def get_color(algname, alt_color_map={}):
if algname in colors:
return colors[algname]
elif algname in alt_color_map:
return alt_color_map[algname]
else:
return np.random.rand(3, )
def get_legend_name(algname, alt_name_map={}):
if algname in names:
return names[algname]
elif algname in alt_name_map:
return alt_name_map[algname]
else:
return algname