mirror of
https://github.com/wassname/kair_algorithms_draft.git
synced 2026-09-11 12:20:26 +08:00
129 lines
3.5 KiB
Python
129 lines
3.5 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""Run module for TD3 on LunarLanderContinuous-v2.
|
|
|
|
- Author: whikwon
|
|
- Contact: whikwon@gmail.com
|
|
"""
|
|
|
|
import torch
|
|
import torch.optim as optim
|
|
|
|
from algorithms.common.networks.mlp import MLP
|
|
from algorithms.common.noise import GaussianNoise
|
|
from algorithms.td3.agent import Agent
|
|
|
|
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
|
|
|
# hyper parameters
|
|
hyper_params = {
|
|
"GAMMA": 0.99,
|
|
"TAU": 5e-3,
|
|
"BUFFER_SIZE": int(1e6),
|
|
"BATCH_SIZE": 100,
|
|
"LR_ACTOR": 1e-3,
|
|
"LR_CRITIC": 1e-3,
|
|
"WEIGHT_DECAY": 0.000,
|
|
"EXPLORATION_NOISE": 0.1,
|
|
"TARGET_POLICY_NOISE": 0.2,
|
|
"TARGET_POLICY_NOISE_CLIP": 0.5,
|
|
"POLICY_UPDATE_FREQ": 2,
|
|
"INITIAL_RANDOM_ACTIONS": 1e4,
|
|
"NETWORK": {"ACTOR_HIDDEN_SIZES": [400, 300], "CRITIC_HIDDEN_SIZES": [400, 300]},
|
|
}
|
|
|
|
|
|
def get(env, args):
|
|
"""Run training or test.
|
|
|
|
Args:
|
|
env (gym.Env): openAI Gym environment with continuous action space
|
|
args (argparse.Namespace): arguments including training settings
|
|
|
|
"""
|
|
state_dim = env.observation_space.shape[0]
|
|
action_dim = env.action_space.shape[0]
|
|
|
|
hidden_sizes_actor = hyper_params["NETWORK"]["ACTOR_HIDDEN_SIZES"]
|
|
hidden_sizes_critic = hyper_params["NETWORK"]["CRITIC_HIDDEN_SIZES"]
|
|
|
|
# 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 critic1
|
|
critic1 = MLP(
|
|
input_size=state_dim + action_dim,
|
|
output_size=1,
|
|
hidden_sizes=hidden_sizes_critic,
|
|
).to(device)
|
|
|
|
critic1_target = MLP(
|
|
input_size=state_dim + action_dim,
|
|
output_size=1,
|
|
hidden_sizes=hidden_sizes_critic,
|
|
).to(device)
|
|
critic1_target.load_state_dict(critic1.state_dict())
|
|
|
|
# create critic2
|
|
critic2 = MLP(
|
|
input_size=state_dim + action_dim,
|
|
output_size=1,
|
|
hidden_sizes=hidden_sizes_critic,
|
|
).to(device)
|
|
|
|
critic2_target = MLP(
|
|
input_size=state_dim + action_dim,
|
|
output_size=1,
|
|
hidden_sizes=hidden_sizes_critic,
|
|
).to(device)
|
|
critic2_target.load_state_dict(critic2.state_dict())
|
|
|
|
# concat critic parameters to use one optim
|
|
critic_parameters = list(critic1.parameters()) + list(critic2.parameters())
|
|
|
|
# create optimizer
|
|
actor_optim = optim.Adam(
|
|
actor.parameters(),
|
|
lr=hyper_params["LR_ACTOR"],
|
|
weight_decay=hyper_params["WEIGHT_DECAY"],
|
|
)
|
|
|
|
critic_optim = optim.Adam(
|
|
critic_parameters,
|
|
lr=hyper_params["LR_CRITIC"],
|
|
weight_decay=hyper_params["WEIGHT_DECAY"],
|
|
)
|
|
|
|
# noise
|
|
exploration_noise = GaussianNoise(
|
|
action_dim,
|
|
min_sigma=hyper_params["EXPLORATION_NOISE"],
|
|
max_sigma=hyper_params["EXPLORATION_NOISE"],
|
|
)
|
|
|
|
target_policy_noise = GaussianNoise(
|
|
action_dim,
|
|
min_sigma=hyper_params["TARGET_POLICY_NOISE"],
|
|
max_sigma=hyper_params["TARGET_POLICY_NOISE"],
|
|
)
|
|
|
|
# make tuples to create an agent
|
|
models = (actor, actor_target, critic1, critic1_target, critic2, critic2_target)
|
|
optims = (actor_optim, critic_optim)
|
|
noises = (exploration_noise, target_policy_noise)
|
|
|
|
# create an agent
|
|
return Agent(env, args, hyper_params, models, optims, noises)
|