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?
python tensorflow keras deep-learning keras-rl
edited Nov 21 at 1:19
Milo Lu
1,57311327
asked Nov 21 at 0:39
CS101
215
add a comment |
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?
python tensorflow keras deep-learning keras-rl
edited Nov 21 at 1:19
Milo Lu
1,57311327
asked Nov 21 at 0:39
CS101
215
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 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 at 1:59
add a comment |
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?
python tensorflow keras deep-learning keras-rl
edited Nov 21 at 1:19
Milo Lu
1,57311327
asked Nov 21 at 0:39
CS101
215
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
python tensorflow keras deep-learning keras-rl
edited Nov 21 at 1:19
Milo Lu
1,57311327
asked Nov 21 at 0:39
CS101
215
edited Nov 21 at 1:19
Milo Lu
1,57311327
asked Nov 21 at 0:39
CS101
215
edited Nov 21 at 1:19
Milo Lu
1,57311327
edited Nov 21 at 1:19
Milo Lu
1,57311327
edited Nov 21 at 1:19
Milo Lu
1,57311327
1,57311327
asked Nov 21 at 0:39
CS101
215
asked Nov 21 at 0:39
CS101
215
asked Nov 21 at 0:39
CS101
215
215
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 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 at 1:59
add a comment |
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 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 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 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 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 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 at 1:59
add a comment |
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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 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 at 1:59