Scale actor network output to the action space bounds in Keras Rl












0















I am trying to implement DDPG from Keras RL and have the following actor network.



actor = Sequential()
actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(nb_actions))
actor.add(Activation('linear'))


However, I would prefer to have the output scaled to a custom gym environment action space bounds for my problem. env.action_space.



https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html shows this using the tflearn api where they use



def create_actor_network(self):
inputs = tflearn.input_data(shape=[None, self.s_dim])
net = tflearn.fully_connected(inputs, 400)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
net = tflearn.fully_connected(net, 300)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
# Final layer weights are init to Uniform[-3e-3, 3e-3]
w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
out = tflearn.fully_connected(
net, self.a_dim, activation='tanh', weights_init=w_init)
# Scale output to -action_bound to action_bound
scaled_out = tf.multiply(out, self.action_bound)
return inputs, out, scaled_out


What is the equivalent command for scaling the output layer according to my requirements?










share|improve this question

























  • I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

    – CS101
    Nov 21 '18 at 1:58













  • If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

    – CS101
    Nov 21 '18 at 1:59
















0















I am trying to implement DDPG from Keras RL and have the following actor network.



actor = Sequential()
actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(nb_actions))
actor.add(Activation('linear'))


However, I would prefer to have the output scaled to a custom gym environment action space bounds for my problem. env.action_space.



https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html shows this using the tflearn api where they use



def create_actor_network(self):
inputs = tflearn.input_data(shape=[None, self.s_dim])
net = tflearn.fully_connected(inputs, 400)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
net = tflearn.fully_connected(net, 300)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
# Final layer weights are init to Uniform[-3e-3, 3e-3]
w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
out = tflearn.fully_connected(
net, self.a_dim, activation='tanh', weights_init=w_init)
# Scale output to -action_bound to action_bound
scaled_out = tf.multiply(out, self.action_bound)
return inputs, out, scaled_out


What is the equivalent command for scaling the output layer according to my requirements?










share|improve this question

























  • I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

    – CS101
    Nov 21 '18 at 1:58













  • If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

    – CS101
    Nov 21 '18 at 1:59














0












0








0








I am trying to implement DDPG from Keras RL and have the following actor network.



actor = Sequential()
actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(nb_actions))
actor.add(Activation('linear'))


However, I would prefer to have the output scaled to a custom gym environment action space bounds for my problem. env.action_space.



https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html shows this using the tflearn api where they use



def create_actor_network(self):
inputs = tflearn.input_data(shape=[None, self.s_dim])
net = tflearn.fully_connected(inputs, 400)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
net = tflearn.fully_connected(net, 300)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
# Final layer weights are init to Uniform[-3e-3, 3e-3]
w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
out = tflearn.fully_connected(
net, self.a_dim, activation='tanh', weights_init=w_init)
# Scale output to -action_bound to action_bound
scaled_out = tf.multiply(out, self.action_bound)
return inputs, out, scaled_out


What is the equivalent command for scaling the output layer according to my requirements?










share|improve this question
















I am trying to implement DDPG from Keras RL and have the following actor network.



actor = Sequential()
actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(16))
actor.add(Activation('relu'))
actor.add(Dense(nb_actions))
actor.add(Activation('linear'))


However, I would prefer to have the output scaled to a custom gym environment action space bounds for my problem. env.action_space.



https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html shows this using the tflearn api where they use



def create_actor_network(self):
inputs = tflearn.input_data(shape=[None, self.s_dim])
net = tflearn.fully_connected(inputs, 400)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
net = tflearn.fully_connected(net, 300)
net = tflearn.layers.normalization.batch_normalization(net)
net = tflearn.activations.relu(net)
# Final layer weights are init to Uniform[-3e-3, 3e-3]
w_init = tflearn.initializations.uniform(minval=-0.003, maxval=0.003)
out = tflearn.fully_connected(
net, self.a_dim, activation='tanh', weights_init=w_init)
# Scale output to -action_bound to action_bound
scaled_out = tf.multiply(out, self.action_bound)
return inputs, out, scaled_out


What is the equivalent command for scaling the output layer according to my requirements?







python tensorflow keras deep-learning keras-rl






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 21 '18 at 1:19









Milo Lu

1,62711527




1,62711527










asked Nov 21 '18 at 0:39









CS101CS101

449




449













  • I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

    – CS101
    Nov 21 '18 at 1:58













  • If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

    – CS101
    Nov 21 '18 at 1:59



















  • I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

    – CS101
    Nov 21 '18 at 1:58













  • If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

    – CS101
    Nov 21 '18 at 1:59

















I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

– CS101
Nov 21 '18 at 1:58







I do not want to write an answer but I realized I have to use a squashing function like tanh or sigmoid to map the network output to a range. Then define a function which will map the value from -1/+1 or 0/1 to the gym environment bounds. This is a more apt method for me since my custom gym environment bounds are not centered around 0

– CS101
Nov 21 '18 at 1:58















If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

– CS101
Nov 21 '18 at 1:59





If my arguement is correct, can anyone tell me what kind of mapping should I define in my defined function. Shoulld I use proportional relationship between -1/+1 and my environment bounds or something more complex?

– CS101
Nov 21 '18 at 1:59












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