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ddpg.py
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import numpy as np
import torch, gym, argparse
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class ReplayBuffer(object):
def __init__(self, max_size=1e6):
self.storage = []
self.max_size = max_size
self.ptr = 0
def add(self, data):
if len(self.storage) == self.max_size:
self.storage[int(self.ptr)] = data
self.ptr = (self.ptr + 1) % self.max_size
else:
self.storage.append(data)
def sample(self, batch_size):
ind = np.random.randint(0, len(self.storage), size=batch_size)
x, y, u, r, d = [], [], [], [], []
for i in ind:
X, Y, U, R, D = self.storage[i]
x.append(np.array(X, copy=False))
y.append(np.array(Y, copy=False))
u.append(np.array(U, copy=False))
r.append(np.array(R, copy=False))
d.append(np.array(D, copy=False))
return np.array(x), np.array(y), np.array(u), np.array(r).reshape(-1, 1), np.array(d).reshape(-1, 1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, action_dim)
self.max_action = max_action
def forward(self, x):
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = self.max_action * torch.tanh(self.l3(x))
return x
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400 + action_dim, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, x, u):
x = F.relu(self.l1(x))
x = F.relu(self.l2(torch.cat([x, u], 1)))
x = self.l3(x)
return x
class DDPG(object):
def __init__(self, state_dim, action_dim, max_action):
self.actor = Actor(state_dim, action_dim, max_action).to(device)
self.actor_target = Actor(state_dim, action_dim, max_action).to(device)
self.actor_target.load_state_dict(self.actor.state_dict())
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(state_dim, action_dim).to(device)
self.critic_target = Critic(state_dim, action_dim).to(device)
self.critic_target.load_state_dict(self.critic.state_dict())
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), weight_decay=1e-2)
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(device)
return self.actor(state).cpu().data.numpy().flatten()
def train(self, replay_buffer, iterations, batch_size=64, discount=0.99, tau=0.001):
for _ in range(iterations):
x, y, u, r, d = replay_buffer.sample(batch_size)
state = torch.FloatTensor(x).to(device)
action = torch.FloatTensor(u).to(device)
next_state = torch.FloatTensor(y).to(device)
done = torch.FloatTensor(1 - d).to(device)
reward = torch.FloatTensor(r).to(device)
target_Q = self.critic_target(next_state, self.actor_target(next_state))
target_Q = reward + (done * discount * target_Q).detach()
current_Q = self.critic(state, action)
critic_loss = F.mse_loss(current_Q, target_Q)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
actor_loss = -self.critic(state, self.actor(state)).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update model
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--env_name", default="Pendulum-v0")
parser.add_argument("--seed", default=0, type=int, help='Sets Gym, PyTorch and Numpy seeds')
parser.add_argument("--start_timesteps", default=1e4, type=int, help='how many step random policy run')
parser.add_argument("--max_timesteps", default=1e6, type=float, help='max_timesteps')
parser.add_argument("--expl_noise", default=0.1, type=float, help='Gaussian exploration')
parser.add_argument("--batch_size", default=100, type=int, help='Batch size')
parser.add_argument("--GAMMA", default=0.99, type=float, help='Discount')
parser.add_argument("--tau", default=0.005, type=float, help='DDPG update rate')
parser.add_argument("--policy_noise", default=0.2, type=float, help='Noise to target policy during critic update')
parser.add_argument("--noise_clip", default=0.5, type=float, help='Range to clip target policy noise')
parser.add_argument("--policy_freq", default=2, type=int, help=' Frequency of delayed policy updates')
args = parser.parse_args()
env = gym.make(args.env_name)
env.seed(args.seed)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
policy = DDPG(state_dim, action_dim, max_action)
replay_buffer = ReplayBuffer()
total_timesteps = 0
timesteps_since_eval = 0
episode_num = 0
episode_reward = 0
episode_timesteps = 0
done = True
while total_timesteps < args.max_timesteps:
if done:
if total_timesteps != 0:
print(("Total T: %d Episode Num: %d Episode T: %d Reward: %f") % (total_timesteps, episode_num, episode_timesteps, episode_reward))
policy.train(replay_buffer, episode_timesteps, args.batch_size, args.GAMMA, args.tau)
obs = env.reset()
done = False
episode_reward = 0
episode_timesteps = 0
episode_num += 1
if total_timesteps < args.start_timesteps:
action = env.action_space.sample()
else:
action = policy.select_action(np.array(obs))
if args.expl_noise != 0:
action = (action + np.random.normal(0, args.expl_noise, size=env.action_space.shape[0])).clip(
env.action_space.low, env.action_space.high)
new_obs, reward, done, _ = env.step(action)
done_bool = 0 if episode_timesteps + 1 == env._max_episode_steps else float(done)
episode_reward += reward
replay_buffer.add((obs, new_obs, action, reward, done_bool))
obs = new_obs
episode_timesteps += 1
total_timesteps += 1
timesteps_since_eval += 1