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added own dqn

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romue 2021-06-17 17:50:15 +02:00
parent 813c9d2c91
commit 84ebd495a6
1 changed files with 13 additions and 15 deletions
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28
algorithms/_base.py
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@ -56,11 +56,11 @@ class BaseDQN(nn.Module):
def __init__(self):
super(BaseDQN, self).__init__()
self.net = nn.Sequential(
nn.Linear(3*5*5, 128),
nn.Linear(3*5*5, 64),
nn.ReLU(),
nn.Linear(128, 128),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(128, 9)
nn.Linear(64, 9)
)
def act(self, x) -> np.ndarray:
@ -81,8 +81,7 @@ class BaseQlearner:
exploration_fraction=0.2, batch_size=64, lr=1e-4, reg_weight=0.0):
self.q_net = q_net
self.target_q_net = target_q_net
#self.q_net.apply(self.weights_init)
polyak_update(self.q_net.parameters(), self.target_q_net.parameters(), 1)
self.q_net.apply(self.weights_init)
self.target_q_net.eval()
self.env = env
self.buffer = buffer
@ -99,8 +98,8 @@ class BaseQlearner:
self.n_agents = n_agents
self.device = 'cpu'
self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=self.lr)
self.running_reward = deque(maxlen=30)
self.running_loss = deque(maxlen=30)
self.running_reward = deque(maxlen=10)
self.running_loss = deque(maxlen=10)
def to(self, device):
self.device = device
@ -149,7 +148,7 @@ class BaseQlearner:
self.train()
if step % self.target_update == 0:
print('UPDATE')
polyak_update(self.q_net.parameters(), self.target_q_net.parameters(), 1.0)
polyak_update(self.q_net.parameters(), self.target_q_net.parameters(), 1)
self.running_reward.append(total_reward)
@ -160,7 +159,7 @@ class BaseQlearner:
def _training_routine(self, obs, next_obs, action):
current_q_values = self.q_net(obs)
current_q_values = torch.gather(current_q_values, dim=1, index=action)
next_q_values_raw = self.target_q_net(next_obs).max(dim=-1)[0].reshape(-1, 1).detach()
next_q_values_raw = self.target_q_net(next_obs).max(dim=1)[0].reshape(-1, 1).detach()
return current_q_values, next_q_values_raw
def train(self):
@ -180,10 +179,9 @@ class BaseQlearner:
)
pred_q += q_values
target_q_raw += next_q_values_raw
target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw
target_q = experience.reward + (1 - experience.done) * self.gamma * target_q_raw
loss = torch.mean(self.reg_weight * pred_q + torch.pow(pred_q - target_q, 2))
#print(target_q)
#print(pred_q.shape, target_q.shape)
# log loss
self.running_loss.append(loss.item())
@ -208,8 +206,8 @@ if __name__ == '__main__':
move_props = MovementProperties(allow_diagonal_movement=True,
allow_square_movement=True,
allow_no_op=False)
env = SimpleFactory(dirt_properties=dirt_props, movement_properties=move_props, n_agents=N_AGENTS, pomdp_radius=2, max_steps=400, omit_agent_slice_in_obs=False)
# env = DummyVecEnv([lambda: env])
env = SimpleFactory(dirt_properties=dirt_props, movement_properties=move_props, n_agents=N_AGENTS, pomdp_radius=2, max_steps=400, omit_agent_slice_in_obs=False, combin_agent_slices_in_obs=True)
#env = DummyVecEnv([lambda: env])
from stable_baselines3.dqn import DQN
#dqn = RegDQN('MlpPolicy', env, verbose=True, buffer_size = 50000, learning_starts = 64, batch_size = 64,
@ -220,6 +218,6 @@ if __name__ == '__main__':
print(env.observation_space, env.action_space)
dqn, target_dqn = BaseDQN(), BaseDQN()
learner = BaseQlearner(dqn, target_dqn, env, BaseBuffer(50000), target_update=10000, lr=0.0001, gamma=0.99, n_agents=N_AGENTS,
learner = BaseQlearner(dqn, target_dqn, env, BaseBuffer(5000), target_update=5000, lr=0.0001, gamma=0.99, n_agents=N_AGENTS,
train_every_n_steps=4, eps_end=0.05, n_grad_steps=1, reg_weight=0.05, exploration_fraction=0.25, batch_size=64)
learner.learn(100000)