import torch
from torch import nn
import numpy as np
import copy
import time
import os
from tqdm import tqdm
from torch_truncnorm.TruncatedNormal import TruncatedNormal
import random
import asyncio
from derk_PPO_LSTM import lstm_agent
from reward_functions import *
device = "cuda:0"
ITERATIONS = 1000000
agent = lstm_agent(512, device)
arm_weapons = [
"Talons", "BloodClaws", "Cleavers", "Cripplers", "Pistol", "Magnum",
"Blaster"
]
misc_weapons = [
"FrogLegs", "IronBubblegum", "HeliumBubblegum", "Shell", "Trombone"
]
tail_weapons = ["HealingGland", "VampireGland", "ParalyzingDart"]
n_arenas = 80
league_size = 10
league = [lstm_agent(512, device) for i in range(league_size)]
device = "cuda:0"
ITERATIONS = 1000000
root_dir = "checkpoints/TEST_LEAGUE_AGENTS"
league = []
member_names = []
k = 30 #ELO weight parameter
k_decay = 0.99
count = 0
for root, dirs, files in os.walk(root_dir):
for name in files: # Load in the agents stored at root_dir
if "best" in name:
temp = lstm_agent(1024, device)
temp.load_state_dict(torch.load(os.path.join(root, name)))
temp.id = count
temp.ELO = 1000
temp.name = name
count += 1
league.append(temp)
random.shuffle(league)
league_size = len(
league
) # Number of policies. Must be even because we don't want byes or anything like that.
assert league_size % 2 == 0, "Number of policies in the TEST_LEAGUE_AGENTS folder must be even"
arm_weapons = [["Pistol", "Magnum", "Blaster"],
["Talons", "BloodClaws", "Cleavers", "Cripplers"],
import time
import torch
from torch import nn
import numpy as np
import copy
import time
import os
from tqdm import tqdm
from torch_truncnorm.TruncatedNormal import TruncatedNormal
import random
from derk_PPO_LSTM import lstm_agent
from reward_functions import *
device = "cuda:0"
ITERATIONS = 1000000
agent = lstm_agent(512, device)
arm_weapons = [
"Talons", "BloodClaws", "Cleavers", "Cripplers", "Pistol", "Magnum",
"Blaster"
]
misc_weapons = [
"FrogLegs", "IronBubblegum", "HeliumBubblegum", "Shell", "Trombone"
]
tail_weapons = ["HealingGland", "VampireGland", "ParalyzingDart"]
n_arenas = 80
random_configs = [{
"slots": [
random.choice(arm_weapons),
random.choice(misc_weapons),
'continuous_entropy_coeff': perturb_explore,
#'continuous_coeff': perturb_explore,
'value_coeff': perturb_explore,
'minibatch_size': minibatch_discrete_perturb_explore,
"lstm_fragment_length": fragment_length_perturb_explore,
}
# Initialize population with uniformly distributed hyperparameters.
population = [
lstm_agent(
1024,
device,
hyperparams={
'learning_rate': 10**np.random.uniform(-5, -3),
'discrete_entropy_coeff': 10**np.random.uniform(-6, -4),
'continuous_entropy_coeff': 10**np.random.uniform(-6, -4),
#'continuous_coeff': 10 ** np.random.uniform(0.8, 2),
'value_coeff': 10**np.random.uniform(-1, 0.3),
'minibatch_size': int(10**np.random.uniform(2.4, 3.6)),
"lstm_fragment_length":
int(random.choice(fragment_length_choices)),
}) for i in range(population_size)
]
print(population[0].get_hyperparams())
# Record the last PBT update
last_PBT_update = [0] * len(population)
model_checkpoint_schedule = [2 * int(i**1.6) for i in range(1000)]
save_folder = "checkpoints/PPO-LSTM-PBT" + str(time.time())
