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update rainbowdqn

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johnjim0816
2022-05-31 01:20:58 +08:00
parent cfc0f6492e
commit c7c94468c9
149 changed files with 1866 additions and 1549 deletions
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20
codes/Sarsa/task0.py
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@@ -5,7 +5,7 @@ Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-11 17:59:16
LastEditor: John
LastEditTime: 2022-04-24 23:03:51
LastEditTime: 2022-04-29 20:18:13
Discription:
Environment:
'''
@@ -31,20 +31,20 @@ class Config:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # check GPU
self.result_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/results/' # path to save results
self.model_path = curr_path+"/outputs/" +self.env_name+'/'+curr_time+'/models/' # path to save models
self.train_eps = 300
self.test_eps = 20
self.train_eps = 300 # training episodes
self.test_eps = 20 # testing episodes
self.n_steps = 200 # maximum steps per episode
self.epsilon_start = 0.90 # start value of epsilon
self.epsilon_end = 0.01 # end value of epsilon
self.epsilon_decay = 200 # decay rate of epsilon
self.gamma = 0.99 # gamma: Gamma discount factor.
self.lr = 0.2 # learning rate: step size parameter
self.n_steps = 200
self.lr = 0.2 # learning rate: step size parameter
self.save = True # if save figures
def env_agent_config(cfg,seed=1):
env = RacetrackEnv()
action_dim = 9
agent = Sarsa(action_dim,cfg)
n_states = 9 # number of actions
agent = Sarsa(n_states,cfg)
return env,agent
def train(cfg,env,agent):
@@ -73,7 +73,7 @@ def train(cfg,env,agent):
print(f"Episode:{i_ep+1}, Reward:{ep_reward}, Epsilon:{agent.epsilon}")
return rewards,ma_rewards
def eval(cfg,env,agent):
def test(cfg,env,agent):
rewards = []
ma_rewards = []
for i_ep in range(cfg.test_eps):
@@ -97,7 +97,7 @@ def eval(cfg,env,agent):
rewards.append(ep_reward)
if (i_ep+1)%1==0:
print("Episode:{}/{}: Reward:{}".format(i_ep+1, cfg.test_eps,ep_reward))
print('Complete evaling！')
print('Complete testing！')
return rewards,ma_rewards
if __name__ == "__main__":
@@ -111,7 +111,7 @@ if __name__ == "__main__":
env,agent = env_agent_config(cfg,seed=10)
agent.load(path=cfg.model_path)
rewards,ma_rewards = eval(cfg,env,agent)
rewards,ma_rewards = test(cfg,env,agent)
save_results(rewards,ma_rewards,tag='test',path=cfg.result_path)
plot_rewards(rewards, ma_rewards, cfg, tag="test")