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td3fd.py
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td3fd.py
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# -*- coding: utf-8 -*-
"""Run module for TD3fD on LunarLanderContinuous-v2.
- Author: Curt Park
- Contact: curt.park@medipixel.io
"""
import argparse
import gym
import torch
import torch.optim as optim
from algorithms.common.networks.mlp import MLP
from algorithms.common.noise import GaussianNoise
from algorithms.fd.td3_agent import Agent
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# hyper parameters
hyper_params = {
"GAMMA": 0.99,
"TAU": 1e-3,
"TARGET_SMOOTHING_NOISE_STD": 0.2,
"TARGET_SMOOTHING_NOISE_CLIP": 0.5,
"DELAYED_UPDATE": 2,
"BUFFER_SIZE": int(1e5),
"BATCH_SIZE": 128,
"LR_ACTOR": 1e-4,
"LR_CRITIC_1": 1e-3,
"LR_CRITIC_2": 1e-3,
"GAUSSIAN_NOISE_MIN_SIGMA": 1.0,
"GAUSSIAN_NOISE_MAX_SIGMA": 1.0,
"GAUSSIAN_NOISE_DECAY_PERIOD": 1000000,
"PRETRAIN_STEP": 0,
"MULTIPLE_LEARN": 1, # multiple learning updates
"LAMDA1": 1.0, # N-step return weight
"LAMDA2": 1e-5, # l2 regularization weight
"LAMDA3": 1.0, # actor loss contribution of prior weight
"PER_ALPHA": 0.3,
"PER_BETA": 1.0,
"PER_EPS": 1e-6,
"EPOCH": 256,
"INITIAL_RANDOM_ACTION": int(1e4),
}
def run(env: gym.Env, args: argparse.Namespace, state_dim: int, action_dim: int):
"""Run training or test.
Args:
env (gym.Env): openAI Gym environment with continuous action space
args (argparse.Namespace): arguments including training settings
state_dim (int): dimension of states
action_dim (int): dimension of actions
"""
hidden_sizes_actor = [256, 256]
hidden_sizes_critic = [256, 256]
# create actor
actor = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target = MLP(
input_size=state_dim,
output_size=action_dim,
hidden_sizes=hidden_sizes_actor,
output_activation=torch.tanh,
).to(device)
actor_target.load_state_dict(actor.state_dict())
# create critic
critic_1 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_2 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target2 = MLP(
input_size=state_dim + action_dim,
output_size=1,
hidden_sizes=hidden_sizes_critic,
).to(device)
critic_target1.load_state_dict(critic_1.state_dict())
critic_target2.load_state_dict(critic_2.state_dict())
# create optimizers
actor_optim = optim.Adam(
actor.parameters(),
lr=hyper_params["LR_ACTOR"],
weight_decay=hyper_params["LAMDA2"],
)
critic_optim1 = optim.Adam(
critic_1.parameters(),
lr=hyper_params["LR_CRITIC_1"],
weight_decay=hyper_params["LAMDA2"],
)
critic_optim2 = optim.Adam(
critic_2.parameters(),
lr=hyper_params["LR_CRITIC_2"],
weight_decay=hyper_params["LAMDA2"],
)
# noise instance to make randomness of action
noise = GaussianNoise(
hyper_params["GAUSSIAN_NOISE_MIN_SIGMA"],
hyper_params["GAUSSIAN_NOISE_MAX_SIGMA"],
hyper_params["GAUSSIAN_NOISE_DECAY_PERIOD"],
)
# make tuples to create an agent
models = (actor, actor_target, critic_1, critic_2, critic_target1, critic_target2)
optims = (actor_optim, critic_optim1, critic_optim2)
# create an agent
agent = Agent(env, args, hyper_params, models, optims, noise)
# run
if args.test:
agent.test()
else:
agent.train()