From 56c367cced253e1cfd44af5ba1304e03bd7f1143 Mon Sep 17 00:00:00 2001 From: pranz24 Date: Sat, 6 Apr 2019 20:28:44 +0530 Subject: [PATCH] Add Value Function --- main.py | 12 +++------ sac.py | 76 +++++++++++++++++++++++---------------------------------- 2 files changed, 35 insertions(+), 53 deletions(-) diff --git a/main.py b/main.py index 2609971..7987544 100644 --- a/main.py +++ b/main.py @@ -12,8 +12,6 @@ from replay_memory import ReplayMemory parser = argparse.ArgumentParser(description='PyTorch REINFORCE example') parser.add_argument('--env-name', default="HalfCheetah-v2", help='name of the environment to run') -parser.add_argument('--policy', default="Gaussian", - help='algorithm to use: Gaussian | Deterministic') parser.add_argument('--eval', type=bool, default=True, help='Evaluates a policy a policy every 10 episode (default:True)') parser.add_argument('--gamma', type=float, default=0.99, metavar='G', @@ -28,7 +26,7 @@ parser.add_argument('--automatic_entropy_tuning', type=bool, default=False, meta help='Temperature parameter α automaically adjusted.') parser.add_argument('--seed', type=int, default=456, metavar='N', help='random seed (default: 456)') -parser.add_argument('--batch_size', type=int, default=256, metavar='N', +parser.add_argument('--batch_size', type=int, default=100, metavar='N', help='batch size (default: 256)') parser.add_argument('--num_steps', type=int, default=1000001, metavar='N', help='maximum number of steps (default: 1000000)') @@ -55,8 +53,7 @@ env.seed(args.seed) # Agent agent = SAC(env.observation_space.shape[0], env.action_space, args) -writer = SummaryWriter(log_dir='runs/{}_SAC_{}_{}_{}'.format(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"), args.env_name, - args.policy, "autotune" if args.automatic_entropy_tuning else "")) +writer = SummaryWriter(log_dir='runs/{}_SAC_{}'.format(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"), args.env_name)) # Memory memory = ReplayMemory(args.replay_size) @@ -82,13 +79,12 @@ for i_episode in itertools.count(1): if len(memory) > args.batch_size: for i in range(args.updates_per_step): # Number of updates per step in environment # Update parameters of all the networks - critic_1_loss, critic_2_loss, policy_loss, ent_loss, alpha = agent.update_parameters(memory, args.batch_size, updates) + value_loss, critic_1_loss, critic_2_loss, policy_loss = agent.update_parameters(memory, args.batch_size, updates) + writer.add_scalar('loss/value', value_loss, updates) writer.add_scalar('loss/critic_1', critic_1_loss, updates) writer.add_scalar('loss/critic_2', critic_2_loss, updates) writer.add_scalar('loss/policy', policy_loss, updates) - writer.add_scalar('loss/entropy_loss', ent_loss, updates) - writer.add_scalar('entropy_temprature/alpha', alpha, updates) updates += 1 next_state, reward, done, _ = env.step(action) # Step diff --git a/sac.py b/sac.py index bc1ab2a..dc86478 100644 --- a/sac.py +++ b/sac.py @@ -3,7 +3,7 @@ import torch import torch.nn.functional as F from torch.optim import Adam from utils import soft_update, hard_update -from model import GaussianPolicy, QNetwork, DeterministicPolicy +from model import GaussianPolicy, QNetwork, ValueNetwork class SAC(object): @@ -13,7 +13,6 @@ class SAC(object): self.tau = args.tau self.alpha = args.alpha - self.policy_type = args.policy self.target_update_interval = args.target_update_interval self.automatic_entropy_tuning = args.automatic_entropy_tuning @@ -22,26 +21,13 @@ class SAC(object): self.critic = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(device=self.device) self.critic_optim = Adam(self.critic.parameters(), lr=args.lr) - self.critic_target = QNetwork(num_inputs, action_space.shape[0], args.hidden_size).to(self.device) - hard_update(self.critic_target, self.critic) + self.value = ValueNetwork(num_inputs, args.hidden_size).to(device=self.device) + self.value_target = ValueNetwork(num_inputs, args.hidden_size).to(self.device) + self.value_optim = Adam(self.value.parameters(), lr=args.lr) + hard_update(self.value_target, self.value) - if self.policy_type == "Gaussian": - # Target Entropy = −dim(A) (e.g. , -6 for HalfCheetah-v2) as given in the paper - if self.automatic_entropy_tuning == True: - self.target_entropy = -torch.prod(torch.Tensor(action_space.shape).to(self.device)).item() - self.log_alpha = torch.zeros(1, requires_grad=True, device=self.device) - self.alpha_optim = Adam([self.log_alpha], lr=args.lr) - - - self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size).to(self.device) - self.policy = GaussianPolicy(self.num_inputs, self.action_space, args.hidden_size).to(self.device) - self.policy_optim = Adam(self.policy.parameters(), lr=args.lr) - - else: - self.alpha = 0 - self.automatic_entropy_tuning = False - self.policy = DeterministicPolicy(num_inputs, action_space.shape[0], args.hidden_size).to(self.device) - self.policy_optim = Adam(self.policy.parameters(), lr=args.lr) + self.policy = GaussianPolicy(num_inputs, action_space.shape[0], args.hidden_size).to(self.device) + self.policy_optim = Adam(self.policy.parameters(), lr=args.lr) @@ -67,15 +53,13 @@ class SAC(object): mask_batch = torch.FloatTensor(mask_batch).to(self.device).unsqueeze(1) with torch.no_grad(): - next_state_action, next_state_log_pi, _ = self.policy.sample(next_state_batch) - qf1_next_target, qf2_next_target = self.critic_target(next_state_batch, next_state_action) - min_qf_next_target = torch.min(qf1_next_target, qf2_next_target) - self.alpha * next_state_log_pi - next_q_value = reward_batch + mask_batch * self.gamma * (min_qf_next_target) + vf_next_target = self.value_target(next_state_batch) + next_q_value = reward_batch + mask_batch * self.gamma * (vf_next_target) qf1, qf2 = self.critic(state_batch, action_batch) # Two Q-functions to mitigate positive bias in the policy improvement step qf1_loss = F.mse_loss(qf1, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2] qf2_loss = F.mse_loss(qf2, next_q_value) # JQ = 𝔼(st,at)~D[0.5(Q1(st,at) - r(st,at) - γ(𝔼st+1~p[V(st+1)]))^2] - + self.critic_optim.zero_grad() qf1_loss.backward() self.critic_optim.step() @@ -91,31 +75,28 @@ class SAC(object): policy_loss = ((self.alpha * log_pi) - min_qf_pi).mean() # Jπ = 𝔼st∼D,εt∼N[α * logπ(f(εt;st)|st) − Q(st,f(εt;st))] + vf = self.value(state_batch) + with torch.no_grad(): + vf_target = min_qf_pi - (self.alpha * log_pi) + + vf_loss = F.mse_loss(vf, vf_target) # JV = 𝔼(st)~D[0.5(V(st) - (𝔼at~π[Q(st,at) - α * logπ(at|st)]))^2] + + self.value_optim.zero_grad() + vf_loss.backward() + self.value_optim.step() + self.policy_optim.zero_grad() policy_loss.backward() self.policy_optim.step() - if self.automatic_entropy_tuning: - alpha_loss = -(self.log_alpha * (log_pi + self.target_entropy).detach()).mean() - - self.alpha_optim.zero_grad() - alpha_loss.backward() - self.alpha_optim.step() - - self.alpha = self.log_alpha.exp() - alpha_tlogs = self.alpha.clone() # For TensorboardX logs - else: - alpha_loss = torch.tensor(0.).to(self.device) - alpha_tlogs = torch.tensor(self.alpha) # For TensorboardX logs - if updates % self.target_update_interval == 0: - soft_update(self.critic_target, self.critic, self.tau) + soft_update(self.value_target, self.value, self.tau) - return qf1_loss.item(), qf2_loss.item(), policy_loss.item(), alpha_loss.item(), alpha_tlogs.item() + return vf_loss.item(), qf1_loss.item(), qf2_loss.item(), policy_loss.item() # Save model parameters - def save_model(self, env_name, suffix="", actor_path=None, critic_path=None): + def save_model(self, env_name, suffix="", actor_path=None, critic_path=None, value_path=None): if not os.path.exists('models/'): os.makedirs('models/') @@ -123,15 +104,20 @@ class SAC(object): actor_path = "models/sac_actor_{}_{}".format(env_name, suffix) if critic_path is None: critic_path = "models/sac_critic_{}_{}".format(env_name, suffix) - print('Saving models to {} and {}'.format(actor_path, critic_path)) + if value_path is None: + value_path = "models/sac_value_{}_{}".format(env_name, suffix) + print('Saving models to {}, {} and {}'.format(actor_path, critic_path, value_path)) torch.save(self.policy.state_dict(), actor_path) torch.save(self.critic.state_dict(), critic_path) + torch.save(self.value.state_dict(), value_path) # Load model parameters - def load_model(self, actor_path, critic_path): - print('Loading models from {} and {}'.format(actor_path, critic_path)) + def load_model(self, actor_path, critic_path, value_path): + print('Loading models from {}, {} and {}'.format(actor_path, critic_path, value_path)) if actor_path is not None: self.policy.load_state_dict(torch.load(actor_path)) if critic_path is not None: self.critic.load_state_dict(torch.load(critic_path)) + if value_path is not None: + self.value.load_state.dict(torch.load(value_path))