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DDPG.py
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DDPG.py
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import os
import sys
import cProfile
import gymnasium as gym
from tqdm import trange
import torch
import torch.nn.functional as F
from utils.common_utils import train_DDPG_agent, read_ckp, ReplayBuffer, PrioritizedReplayBuffer
import numpy as np
import random
import argparse
import warnings
warnings.filterwarnings('ignore')
class PolicyNet(torch.nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim, action_dim)
self.low = low
self.high = high
def forward(self, x):
x = F.relu(self.fc1(x))
action = torch.tanh(self.fc2(x))
action = self.low + (action + 1.0) * (self.high - self.low) / 2 # Zoom to the specified space
return action
class QValueNet(torch.nn.Module):
def __init__(self, state_dim, hidden_dim, action_dim):
super().__init__()
self.fc1 = torch.nn.Linear(state_dim + action_dim, hidden_dim)
self.fc2 = torch.nn.Linear(hidden_dim, hidden_dim)
self.fc_out = torch.nn.Linear(hidden_dim, 1)
def forward(self, x, a):
cat = torch.cat([x, a], dim=1)
x = F.relu(self.fc1(cat))
x = F.relu(self.fc2(x))
return self.fc_out(x)
class DDPG:
''' DDPG '''
def __init__(self, state_dim, hidden_dim, action_dim,sigma, actor_lr,
critic_lr, tau, gamma, device, training=True):
self.training = training
self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
self.critic = QValueNet(state_dim, hidden_dim, action_dim).to(device)
self.target_actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device)
self.target_critic = QValueNet(state_dim, hidden_dim, action_dim).to(device)
self.target_critic.load_state_dict(self.critic.state_dict())
self.target_actor.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr)
self.gamma = gamma
self.sigma = sigma
self.tau = tau
self.action_dim = action_dim
self.device = device
def take_action(self, state, the_device=None):
state = torch.tensor(state) if not isinstance(state, torch.Tensor) else state
state = state.to(the_device) if the_device else state.to(self.device)
action = self.actor(state).detach().cpu()
if self.training:
action = action + self.sigma * np.random.randn(self.action_dim)
return action
def soft_update(self, net, target_net):
'''Soft update target_net in the direction of net, each update is very small
Params
----------
net : torch.nn.module
target_net : torch.nn.module
'''
for param_target, param in zip(target_net.parameters(), net.parameters()):
param_target.data.copy_(param_target.data * (1.0 - self.tau) + param.data * self.tau)
def update(self, transition_dict):
states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)
actions = torch.tensor(transition_dict['actions'], dtype=torch.float).to(self.device)
rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)
next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)
dones = torch.tensor(transition_dict['dones'], dtype=torch.int).view(-1, 1).to(self.device)
truncated = torch.tensor(transition_dict['truncated'], dtype=torch.int).view(-1, 1).to(self.device)
weights = torch.FloatTensor(transition_dict["weights"].reshape(-1, 1)).to(self.device)
next_q_values = self.target_critic(next_states, self.target_actor(next_states))
q_targets = rewards + self.gamma * next_q_values * (1 - dones | truncated)
critic_losses = F.mse_loss(self.critic(states, actions), q_targets.detach(), reduction='none')
critic_loss = torch.mean(critic_losses * weights)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
actor_loss = -torch.mean(self.critic(states, self.actor(states)))
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.soft_update(self.actor, self.target_actor)
self.soft_update(self.critic, self.target_critic)
return critic_losses.detach().cpu()
# * --------------------- 参数 -------------------------
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='DDPG task')
parser.add_argument('--model_name', default="DDPG", type=str, help='Alg name')
parser.add_argument('-t', '--task', default="lunar", type=str, help='Task name')
parser.add_argument('-w', '--writer', default=0, type=int, help='Saving type, 0: No，1: local')
parser.add_argument('--sta', action="store_true", help='Whether to use STA')
parser.add_argument('--per', action="store_true", help='Whether to use PER')
parser.add_argument('-d', '--distance_ratio', default=0.2, type=float, help='Generate experience ratio')
parser.add_argument('-n', '--num_new_samples', default=3, type=int, help='The number of counterfactual actions sampled')
parser.add_argument('-e', '--episodes', default=300, type=int, help='episodes')
parser.add_argument('-s', '--seed', nargs='+', default=[1, 7], type=int, help='Start and end seeds')
args = parser.parse_args()
# env
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# Continuous Action Space Environment Benchmark
if args.task == 'pendulum':
env = gym.make('Pendulum-v1')
elif args.task == 'lunar':
env = gym.make("LunarLander-v2", continuous=True)
elif args.task == 'walker':
env = gym.make("BipedalWalker-v3")
# DDPG
action_type = 'discrete' if isinstance(env.action_space, gym.spaces.Discrete) else 'continuous'
actor_lr = 2e-4
critic_lr = 2e-4
hidden_dim = 128
buffer_size = int(5e4)
minimal_size = buffer_size // 10
gamma = 0.98
sigma = 0.01
tau = 0.005
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
if (abs(env.action_space.high) != abs(env.action_space.low)).any():
print('WARNING: Action space asymmetry')
# action bound
low = torch.tensor(env.action_space.low, device=device)
high = torch.tensor(env.action_space.high, device=device)
# task
total_epochs = 1
batch_size = 128
system_type = sys.platform
# CEA params
if args.sta:
path = f'model/sta/{args.task}/regular.pt'
sta = torch.load(path, map_location=device)
if args.per:
args.model_name = f'CEA_{args.model_name}'
else:
args.model_name = f'CEA_{args.model_name}-no_PER'
else:
if args.per:
args.model_name = f'{args.model_name}_PER'
sta = False
print(f'[ Start training, task: {args.task}, alg: {args.model_name}, device: {device} ]')
# * ----------------------- 训练 ----------------------------
for seed in trange(args.seed[0], args.seed[-1] + 1, mininterval=40, ncols=100):
CKP_PATH = f'ckpt/{args.task}/{args.model_name}/{seed}_{system_type}.pt'
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if args.per:
replay_buffer = PrioritizedReplayBuffer(state_dim, action_dim, buffer_size, batch_size)
else:
replay_buffer = ReplayBuffer(state_dim, action_dim, buffer_size, batch_size)
agent = DDPG(state_dim, hidden_dim, action_dim, sigma, actor_lr,
critic_lr, tau, gamma, device)
s_epoch, s_episode, return_list, time_list, seed_list = read_ckp(CKP_PATH, agent, args.model_name)
# cProfile.run('''return_list, train_time = train_DDPG_agent(env, agent, args.writer, s_epoch, total_epochs,
# s_episode, args.episodes, replay_buffer, minimal_size,
# return_list, time_list, seed_list, seed, CKP_PATH,
# sta, args.per, args.num_new_samples, args.distance_ratio
# )''', './DDPG_analysis')
return_list, train_time = train_DDPG_agent(env, agent, args.writer, s_epoch, total_epochs,
s_episode, args.episodes, replay_buffer, minimal_size,
return_list, time_list, seed_list, seed, CKP_PATH,
sta, args.per, args.num_new_samples, args.distance_ratio
)