根据上述视频可以看出,在默认的DQN网络及参数,还不能使飞行器稳定停在月球上,将学习率改为5e-4,网络参数改为256,训练次数改为2500,000次,训练代码如下: importgymfromstable_baselines3importDQN# Create environmentenv=gym.make("LunarLander-v2")model=DQN("MlpPolicy",env,verbose=1,learning_rate=5e-4,polic...
采用DQN算法进行目标车辆的决策控制,模型训练代码如下: importgymimporthighway_envfromstable_baselines3importDQN# Create environmentenv=gym.make("highway-fast-v0")model=DQN('MlpPolicy',env,policy_kwargs=dict(net_arch=[256,256]),learning_rate=5e-4,buffer_size=15000,learning_starts=200,batch_size=...
I am trying to implement the DQN algorithm using the "stable_baselines3" library, but I am encountering difficulties because the model starts to spam the same cicle of letters at every episode, and I cannot understand why. The environment is custom; I wrote it myself, so there might be e...
statesj的合法状态空间为Aj⊆A，非法状态空间为Aj∁，那么当选择到ak∈Aj∁时，重新选择alegal=ak...
❓ Question Hello, I am trying to log Q-values using custom callback, but I am new in this field and not sure the code below is the correct way to do it. class CustomLoggingCallback(BaseCallback): def __init__(self, verbose=1): super(Cust...
(from matplotlib->stable-baselines3==1.5.0->-r /home/aistudio/work/requirements.txt (line 4)) (1.1.0) Requirement already satisfied: six>=1.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from matplotlib->stable-baselines3==1.5.0->-r /home/aistudio/work/...
I2A outperforms a number of baselines, including the MCTS (Monte Carlo Tree Search) planning algorithm. It is also able to perform well in experiments where its model-based component is intentionally restricted to make poor predictions, demonstrating that it is able to trade-off use of the mod...
- stable_baselines3.common.atari_wrappers.AtariWrapper frame_stack: 4 policy: 'CnnPolicy' n_timesteps: !!float 1e7 buffer_size: 100000 learning_rate: !!float 1e-4 batch_size: 32 learning_starts: 100000 target_update_interval: 1000 train_freq: 4 gradient_steps: 1 exploration_fraction: 0.1...
这部分可由stable_baseline3实现: from stable_baselines3.common.buffers import ReplayBuffer # 初始化Buffer rb = ReplayBuffer( args.buffer_size, envs.single_observation_space, envs.single_action_space, device, handle_timeout_termination=True, ) #向Buffer中添加转移四元对 rb.add(obs, real_next_...
关于算法,我打算直接使用 Stable Baselines3 提供的 DQN 算法作为模型进行训练。 为了让 Atari 能够在 Colab 上正常运行,我们需要先让 gym[Atari] 获取Colab 的 ROM。具体操作如下: 该段代码来自此 Github ! wget http://www.atarimania.com/roms/Roms.rar ! mkdir /content/ROM/ ! unrar e /content/Roms....