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hot update A2C

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johnjim0816
2022-08-29 15:12:33 +08:00
parent 99a3c1afec
commit 0b0f7e857d
109 changed files with 8213 additions and 1658 deletions
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18
projects/codes/PolicyGradient/main.py
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@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2022-08-25 20:59:23
LastEditTime: 2022-08-27 00:04:08
Discription:
Environment:
'''
@@ -34,7 +34,7 @@ class PGNet(MLP):
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.sigmoid(self.fc3(x))
x = torch.sigmoid(self.fc3(x))
return x
class Main(Launcher):
@@ -47,8 +47,9 @@ class Main(Launcher):
parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
parser.add_argument('--ep_max_steps',default = 100000,type=int,help="steps per episode, much larger value can simulate infinite steps")
parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
parser.add_argument('--lr',default=0.005,type=float,help="learning rate")
parser.add_argument('--lr',default=0.01,type=float,help="learning rate")
parser.add_argument('--update_fre',default=8,type=int)
parser.add_argument('--hidden_dim',default=36,type=int)
parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda")
@@ -81,7 +82,7 @@ class Main(Launcher):
for i_ep in range(cfg['train_eps']):
state = env.reset()
ep_reward = 0
for _ in count():
for _ in range(cfg['ep_max_steps']):
action = agent.sample_action(state) # sample action
next_state, reward, done, _ = env.step(action)
ep_reward += reward
@@ -90,8 +91,9 @@ class Main(Launcher):
agent.memory.push((state,float(action),reward))
state = next_state
if done:
print(f"Episode：{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}")
break
if (i_ep+1) % 10 == 0:
print(f"Episode：{i_ep+1}/{cfg['train_eps']}, Reward:{ep_reward:.2f}")
if (i_ep+1) % cfg['update_fre'] == 0:
agent.update()
rewards.append(ep_reward)
@@ -107,7 +109,7 @@ class Main(Launcher):
for i_ep in range(cfg['test_eps']):
state = env.reset()
ep_reward = 0
for _ in count():
for _ in range(cfg['ep_max_steps']):
action = agent.predict_action(state)
next_state, reward, done, _ = env.step(action)
ep_reward += reward
@@ -115,9 +117,9 @@ class Main(Launcher):
reward = 0
state = next_state
if done:
print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
break
rewards.append(ep_reward)
print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
rewards.append(ep_reward)
print("Finish testing!")
env.close()
return {'episodes':range(len(rewards)),'rewards':rewards}