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hot update DQN

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This commit is contained in:
johnjim0816
2022-08-24 12:49:16 +08:00
parent 07fb1d233e
commit 4f4658503e
24 changed files with 148 additions and 512 deletions
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18
projects/codes/DQN/main.py
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@@ -27,7 +27,7 @@ def get_args():
parser.add_argument('--gamma',default=0.95,type=float,help="discounted factor")
parser.add_argument('--epsilon_start',default=0.95,type=float,help="initial value of epsilon")
parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon")
parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon")
parser.add_argument('--epsilon_decay',default=500,type=int,help="decay rate of epsilon, the higher value, the slower decay")
parser.add_argument('--lr',default=0.0001,type=float,help="learning rate")
parser.add_argument('--memory_capacity',default=100000,type=int,help="memory capacity")
parser.add_argument('--batch_size',default=64,type=int)
@@ -64,8 +64,8 @@ def env_agent_config(cfg):
def train(cfg, env, agent):
''' 训练
'''
print("start training!")
print(f"Env: {cfg['env_name']}, Algo: {cfg['algo_name']}, Device: {cfg['device']}")
print("Start training!")
print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
rewards = [] # record rewards for all episodes
steps = []
for i_ep in range(cfg["train_eps"]):
@@ -89,17 +89,17 @@ def train(cfg, env, agent):
rewards.append(ep_reward)
if (i_ep + 1) % 10 == 0:
print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}: Epislon: {agent.epsilon:.3f}')
print("finish training!")
print("Finish training!")
env.close()
res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
return res_dic
def test(cfg, env, agent):
print("start testing!")
print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
print("Start testing!")
print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
rewards = [] # record rewards for all episodes
steps = []
for i_ep in range(cfg.test_eps):
for i_ep in range(cfg['test_eps']):
ep_reward = 0 # reward per episode
ep_step = 0
state = env.reset() # reset and obtain initial state
@@ -113,8 +113,8 @@ def test(cfg, env, agent):
break
steps.append(ep_step)
rewards.append(ep_reward)
print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
print("finish testing!")
print(f"Episode: {i_ep+1}/{cfg['test_eps']}，Reward: {ep_reward:.2f}")
print("Finish testing!")
env.close()
return {'episodes':range(len(rewards)),'rewards':rewards}