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constants.py
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constants.py
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# tags
x_tag = ("z/env_steps", "env_steps")
y_tags = [ ### raw_atg, processed tag
## reward/succ tags
("metrics/return_eval_total", "return"),
# ("metrics/return_train_total", "return_train"),
# ("metrics/success_rate_eval", "success_rate"),
("metrics/return_eval_avg", "return_avg"),
("metrics/return_eval_worst", "return_worst"),
("metrics/succ_eval_DD", "succ_DD"),
("metrics/succ_eval_DR", "succ_DR"),
("metrics/succ_eval_DE", "succ_DE"),
("metrics/succ_eval_RD", "succ_RD"),
("metrics/succ_eval_RR", "succ_RR"),
("metrics/succ_eval_RE", "succ_RE"),
("metrics/succ_eval_EE", "succ_EE"),
]
generalization_tags = {
"Default": ["succ_DD"],
"Interpolation": ["succ_RR"],
"Extrapolation": ["succ_DR", "succ_DE", "succ_RE"],
}
method_tag = "method"
trial_tag = "trial"
# create a merged tag
merged_tag = "instance"
auc_tag = "-auc"
variant_tag_names = [
"RL",
"Encoder",
"Len",
"Inputs",
"Arch",
]
diagnosis_tags = [
## loss/gradient tags
# ("rl_loss/qf1_loss", "q_loss"),
# ("rl_loss/policy_loss", "pi_loss"), # pi loss can be negative, so we don't plot it
# for shared ac
("rl_loss/rnn_grad_norm", "rnn_grad_norm"),
# for separate ac
("rl_loss/q_rnn_grad_norm", "q_rnn_grad_norm"),
("rl_loss/pi_rnn_grad_norm", "pi_rnn_grad_norm"),
]
ours_name = "Ours: recurrent model-free RL"
baseline_names = ["sac_gru", "sac_lstm", "sacd_lstm", "td3_gru", "td3_lstm"]
def get_variant_tags(trial_str, max_episode_len):
v = dict()
# find RL tag
if "sac_" in trial_str:
v["RL"] = "sac"
elif "sacd_" in trial_str:
v["RL"] = "sacd"
elif "td3" in trial_str:
v["RL"] = "td3"
if "lstm" in trial_str:
v["Encoder"] = "lstm"
elif "gru" in trial_str:
v["Encoder"] = "gru"
if "shared" in trial_str:
v["Arch"] = "shared"
else:
v["Arch"] = "separate"
v["Len"] = int(trial_str[trial_str.index("len-") + 4 :].split("/")[0])
if v["Len"] == -1:
v["Len"] = max_episode_len
v["Inputs"] = trial_str.split("/")[-3]
return v
def get_env_tag(path):
if "pomdp" in path:
if "/P/" in path:
suffix = "P"
else:
suffix = "V"
if "Ant" in path:
prefix = "Ant"
elif "HalfCheetah" in path:
prefix = "Cheetah"
elif "Hopper" in path:
prefix = "Hopper"
elif "Walker" in path:
prefix = "Walker"
else:
raise ValueError
return prefix + "-" + suffix
elif "meta" in path:
if "PointRobotSparse" in path:
return "Semi-Circle"
elif "Wind" in path:
return "Wind"
elif "HalfCheetahVel" in path:
return "Cheetah-Vel"
elif "AntDir" in path:
return "Ant-Dir"
elif "CheetahDir" in path:
return "Cheetah-Dir"
elif "HumanoidDir" in path:
return "Humanoid-Dir"
elif "rmdp" in path:
if "HalfCheetah" in path:
prefix = "Cheetah"
elif "Hopper" in path:
prefix = "Hopper"
elif "Walker" in path:
prefix = "Walker"
return prefix + "-" + "Robust"
elif "generalize" in path:
if "Cheetah" in path:
prefix = "Cheetah"
elif "Hopper" in path:
prefix = "Hopper"
return prefix + "-" + "Generalize"
# the content will not be used, not important
specialized_tags = {
"onpolicy-varibad": {
"RL": "ppo",
"Encoder": "gru",
"Len": "-1",
"Inputs": "oard",
"Arch": "separate",
},
"oracle_ppo": {
"RL": "ppo",
"Encoder": "mlp",
"Len": "1",
"Inputs": "s",
"Arch": "separate",
}, # from onpolicy varibad data
"offpolicy-varibad": {
"RL": "sac",
"Encoder": "gru",
"Len": "-1",
"Inputs": "oard",
"Arch": "separate",
},
"re-varibad": {
"RL": "sac",
"Encoder": "gru",
"Len": "-1",
"Inputs": "oard",
"Arch": "separate",
}, # discarded as re-implemented off-varibad
"rl2": {
"RL": "ppo",
"Encoder": "gru",
"Len": "-1",
"Inputs": "oard",
"Arch": "separate",
},
"VRM": {
"RL": "sac",
"Encoder": "lstm",
"Len": "64",
"Inputs": "oar",
"Arch": "separate",
},
"MRPO": {
"RL": "ppo",
"Encoder": "no",
"Len": "1",
"Inputs": "s",
"Arch": "separate",
},
"ppo_gru": {
"RL": "ppo",
"Encoder": "gru",
"Len": "128",
"Inputs": "o",
"Arch": "shared",
},
"a2c_gru": {
"RL": "a2c",
"Encoder": "gru",
"Len": "5",
"Inputs": "o",
"Arch": "shared",
},
"sac_mlp": {
"RL": "sac",
"Encoder": "no",
"Len": "1",
"Inputs": "o",
"Arch": "separate",
},
"td3_mlp": {
"RL": "td3",
"Encoder": "no",
"Len": "1",
"Inputs": "o",
"Arch": "separate",
},
"Markovian_sac": {
"RL": "sac",
"Encoder": "no",
"Len": "1",
"Inputs": "o",
"Arch": "separate",
},
"Markovian_td3": {
"RL": "td3",
"Encoder": "no",
"Len": "1",
"Inputs": "o",
"Arch": "separate",
},
"oracle_sac": {
"RL": "sac",
"Encoder": "no",
"Len": "1",
"Inputs": "s",
"Arch": "separate",
},
"oracle_td3": {
"RL": "td3",
"Encoder": "no",
"Len": "1",
"Inputs": "s",
"Arch": "separate",
},
"oracle_rnn": {
"RL": "RL",
"Encoder": "rnn",
"Len": "64",
"Inputs": "s",
"Arch": "separate",
},
"td3_2lstm": {
"RL": "td3",
"Encoder": "2lstm",
"Len": "64",
"Inputs": "o",
"Arch": "separate",
},
"td3_2gru": {
"RL": "td3",
"Encoder": "2gru",
"Len": "64",
"Inputs": "oa",
"Arch": "separate",
},
"td3_pfgru": {
"RL": "td3",
"Encoder": "pfgru",
"Len": "64",
"Inputs": "oa",
"Arch": "separate",
},
}
# best plots
no_dash = (1, 0)
dashes1 = (2, 1)
dashes2 = (4, 1)
curve_style_dict = {
# key: (color, dash)
ours_name: ("blue", no_dash),
"oracle_rnn": ("magenta", no_dash),
"VRM": ("green", no_dash),
"ppo_gru": ("magenta", no_dash),
"a2c_gru": ("yellow", no_dash),
"MRPO": ("red", no_dash),
"onpolicy-varibad": ("green", no_dash),
"offpolicy-varibad": ("red", no_dash),
"re-varibad": ("yellow", no_dash),
"rl2": ("magenta", no_dash),
"oracle_ppo": ("yellow", no_dash),
"A2C-RC": ("yellow", no_dash),
"EPOpt-PPO-FF": ("red", no_dash),
"Oracle": ("orange", no_dash),
"Markovian": ("black", no_dash),
"Random": ("purple", no_dash),
"sac_mlp": ("black", no_dash),
"td3_mlp": ("purple", no_dash),
"Markovian_sac": ("black", no_dash),
"Markovian_td3": ("purple", no_dash),
"oracle_sac": ("orange", no_dash),
"oracle_td3": ("pink", no_dash),
"td3_2lstm": ("brown", no_dash),
"td3_2gru": ("brown", no_dash),
"td3_pfgru": ("magenta", no_dash),
"IMPALA+SR": ("red", no_dash),
}
# ablation plots
variant_colors = {
"RL": {"sac": "red", "sacd": "red", "td3": "blue"},
"Encoder": {"gru": "red", "lstm": "blue"},
"Inputs": {"o": "yellow", "or": "green", "oa": "red", "oar": "blue"},
"Arch": {"shared": "red", "separate": "blue"},
}
table_results = {
"Ant-Dir": {
"return": {"Random": -48.247},
},
"Cheetah-Dir": {
"return": {"Random": -39.3},
},
"Humanoid-Dir": {
"return": {"Random": 296.991},
},
"Cheetah-Vel": {
"return": {"Oracle": -79.692, "Markovian": -351.654, "Random": -646.3391},
},
"Semi-Circle": {
"return": {"Oracle": 103.347, "Markovian": 12.433, "Random": 3.0},
},
"Wind": {
"return": {"Oracle": 63.695, "Markovian": 18.351, "Random": 0.0},
},
"Ant-P": {
"return": {"Oracle": 3394, "Markovian": 1132.946, "Random": 372.6779},
},
"Ant-V": {
"return": {"Oracle": 3394, "Markovian": 645.05, "Random": 372.6779},
},
"Cheetah-P": {
"return": {"Oracle": 2994, "Markovian": 870.48, "Random": -1256.6604},
},
"Cheetah-V": {
"return": {"Oracle": 2994, "Markovian": 517.946, "Random": -1256.6604},
},
"Hopper-P": {
"return": {"Oracle": 2434, "Markovian": 477.911, "Random": 19.9634},
},
"Hopper-V": {
"return": {"Oracle": 2434, "Markovian": 228.366, "Random": 19.9634},
},
"Walker-P": {
"return": {"Oracle": 2225, "Markovian": 471.815, "Random": 16.5319},
},
"Walker-V": {
"return": {"Oracle": 2225, "Markovian": 214.45, "Random": 16.5319},
},
"Cheetah-Robust": {
"return_avg": {"Random": 1.3098}, # "Oracle": 1372.335, "Markovian": 1363.131,
"return_worst": {"Random": -5.6627}, # "Oracle": 896.129, "Markovian": 807.229,
},
"Hopper-Robust": {
"return_avg": {"Random": 21.262}, # "Oracle": 1409.703, "Markovian": 1438.069,
"return_worst": {"Random": 16.703}, # "Oracle": 427.483, "Markovian": 476.617,
},
"Walker-Robust": {
"return_avg": {"Random": 16.3323}, # "Oracle": 1146.887, "Markovian": 1159.432,
"return_worst": {"Random": 12.3943}, # "Oracle": 545.257, "Markovian": 570.559,
},
"Cheetah-Generalize": {
"Default": {"A2C-RC": 0.8806, "EPOpt-PPO-FF": 0.9976},
"Interpolation": {
"A2C-RC": 0.7470,
"EPOpt-PPO-FF": 0.9928,
# "Oracle": 0.255, "Markovian": 0.642,
"Random": 0.0,
},
"Extrapolation": {
"A2C-RC": 0.4296,
"EPOpt-PPO-FF": 0.5341,
# "Oracle": 0.1323, "Markovian": 0.293,
"Random": 0.0,
},
},
"Hopper-Generalize": {
"Default": {"A2C-RC": 0.1534, "EPOpt-PPO-FF": 0.5862},
"Interpolation": {
"A2C-RC": 0.1038,
"EPOpt-PPO-FF": 0.8078,
# "Oracle": 0.466, "Markovian": 0.307,
"Random": 0.0,
},
"Extrapolation": {
"A2C-RC": 0.0131,
"EPOpt-PPO-FF": 0.2139,
# "Oracle": 0.2847, "Markovian": 0.245,
"Random": 0.0,
},
},
"Delayed-Catch": {
"return": {"IMPALA+SR": 3.1, "Random": 5.93}, # at 2.5M
},
"Key-to-Door": {
"success_rate": {"IMPALA+SR": 0.57, "Random": 0.022}, # at 4M
},
}