我试图使用keras使用LSTM实现A3C模型，我使用此版本的A3C而不使用LSTM：＆＃34; https://github.com/coreylynch/async-rl＆＃34;，并尝试仅修改网络代码，但我很难编译整个模型：
我错过了什么吗？这是我的模特：
state = tf.placeholder("float", [None, agent_history_length, resized_width, resized_height])
vision_model = Sequential()
vision_model.add(Conv2D(activation="relu", filters=16, kernel_size=(8, 8), name="conv1", padding="same", strides=(4, 4),input_shape=(agent_history_length,resized_width, resized_height)))
vision_model.add(Conv2D(activation="relu", filters=32, kernel_size=(4, 4), name="conv2", padding="same", strides=(2, 2)))
vision_model.add(Flatten())
vision_model.add(Dense(activation="relu", units=256, name="h1"))
# Now let's get a tensor with the output of our vision model:
state_input = Input(shape=(1,agent_history_length,resized_width,resized_height))
encoded_frame_sequence = TimeDistributed(vision_model)(state_input)
encoded_video = LSTM(256)(encoded_frame_sequence) # the output will be a vector
action_probs = Dense(activation="softmax", units=4, name="p")(encoded_video)
state_value = Dense(activation="linear", units=1, name="v")(encoded_video)
policy_network = Model(inputs=state_input, outputs=action_probs)
value_network = Model(inputs=state_input, outputs=state_value)
p_params = policy_network.trainable_weights
v_params = value_network.trainable_weights
policy_network.summary()
value_network.summary()
p_out = policy_network(state_input)
v_out = value_network(state_input)
答案 0 :(得分：0)
keras-rl示例lib不支持超过2D输入形状！