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fix action shape problem

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This commit is contained in:
Morvan Zhou
2017-10-30 09:58:09 +11:00
committed by Morvan Zhou
parent a7b14b8091
commit 9318a69f08
1 changed files with 6 additions and 3 deletions
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9
tutorial-contents/405_DQN_Reinforcement_learning.py
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@ -26,14 +26,15 @@ env = gym.make('CartPole-v0')
env = env.unwrapped env = env.unwrapped
N_ACTIONS = env.action_space.n N_ACTIONS = env.action_space.n
N_STATES = env.observation_space.shape[0] N_STATES = env.observation_space.shape[0]
ENV_A_SHAPE = 0 if isinstance(env.action_space.sample(), int) else env.action_space.sample().shape # to confirm the shape
class Net(nn.Module): class Net(nn.Module):
def __init__(self, ): def __init__(self, ):
super(Net, self).__init__() super(Net, self).__init__()
self.fc1 = nn.Linear(N_STATES, 10) self.fc1 = nn.Linear(N_STATES, 50)
self.fc1.weight.data.normal_(0, 0.1) # initialization self.fc1.weight.data.normal_(0, 0.1) # initialization
self.out = nn.Linear(10, N_ACTIONS) self.out = nn.Linear(50, N_ACTIONS)
self.out.weight.data.normal_(0, 0.1) # initialization self.out.weight.data.normal_(0, 0.1) # initialization
def forward(self, x): def forward(self, x):
@ -58,9 +59,11 @@ class DQN(object):
# input only one sample # input only one sample
if np.random.uniform() < EPSILON: # greedy if np.random.uniform() < EPSILON: # greedy
actions_value = self.eval_net.forward(x) actions_value = self.eval_net.forward(x)
action = torch.max(actions_value, 1)[1].data.numpy()[0, 0] # return the argmax action = torch.max(actions_value, 1)[1].data.numpy()
action = action[0, 0] if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE) # return the argmax index
else: # random else: # random
action = np.random.randint(0, N_ACTIONS) action = np.random.randint(0, N_ACTIONS)
action = action if ENV_A_SHAPE == 0 else action.reshape(ENV_A_SHAPE)
return action return action
def store_transition(self, s, a, r, s_): def store_transition(self, s, a, r, s_):