Beginner Explanation
Imagine you’re trying to steer a toy car on a smooth track. Instead of just pressing buttons for left or right, you can gently turn the steering wheel at any angle. This is like continuous control in reinforcement learning, where an AI agent can choose any action within a range, not just a few fixed options. It helps robots and AI systems learn to make smooth, precise movements in real-world tasks, like walking or driving.Technical Explanation
Continuous control tasks involve decision-making in environments where actions are not limited to discrete choices. For instance, in a robotic arm manipulation task, the angles of the joints can take any value within a range. Common algorithms for continuous control include Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO). Here’s a simple implementation of DDPG using TensorFlow: “`python import numpy as np import tensorflow as tf from tensorflow.keras import layers # Define a simple DDPG agent class DDPGAgent: def __init__(self, action_space): self.action_space = action_space self.actor = self.build_actor() self.critic = self.build_critic() def build_actor(self): model = tf.keras.Sequential([ layers.Dense(256, activation=’relu’), layers.Dense(256, activation=’relu’), layers.Dense(self.action_space, activation=’tanh’) ]) return model def build_critic(self): model = tf.keras.Sequential([ layers.Dense(256, activation=’relu’), layers.Dense(256, activation=’relu’), layers.Dense(1) ]) return model # Usage agent = DDPGAgent(action_space=2) # For example, a 2D action space “`Academic Context
Continuous control problems are a significant area of research in reinforcement learning, particularly in robotics and autonomous systems. The foundational work includes the use of policy gradient methods, which allow for the optimization of continuous action distributions. Key papers include ‘Continuous Control with Deep Reinforcement Learning’ by Lillicrap et al. (2015), which introduced the DDPG algorithm, and ‘Proximal Policy Optimization Algorithms’ by Schulman et al. (2017), which provides a more stable approach to policy optimization. The mathematical foundation often relies on stochastic policy optimization and the Bellman equation, with the objective of maximizing expected cumulative rewards over continuous action spaces.Code Examples
Example 1:
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# Define a simple DDPG agent
class DDPGAgent:
def __init__(self, action_space):
self.action_space = action_space
self.actor = self.build_actor()
self.critic = self.build_critic()
def build_actor(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(self.action_space, activation='tanh')
])
return model
def build_critic(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(1)
])
return model
# Usage
agent = DDPGAgent(action_space=2) # For example, a 2D action spaceExample 2:
def __init__(self, action_space):
self.action_space = action_space
self.actor = self.build_actor()
self.critic = self.build_critic()Example 3:
def build_actor(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(self.action_space, activation='tanh')
])
return modelExample 4:
def build_critic(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(1)
])
return modelExample 5:
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
# Define a simple DDPG agentExample 6:
import tensorflow as tf
from tensorflow.keras import layers
# Define a simple DDPG agent
class DDPGAgent:Example 7:
from tensorflow.keras import layers
# Define a simple DDPG agent
class DDPGAgent:
def __init__(self, action_space):Example 8:
class DDPGAgent:
def __init__(self, action_space):
self.action_space = action_space
self.actor = self.build_actor()
self.critic = self.build_critic()Example 9:
def __init__(self, action_space):
self.action_space = action_space
self.actor = self.build_actor()
self.critic = self.build_critic()
Example 10:
def build_actor(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(self.action_space, activation='tanh')Example 11:
def build_critic(self):
model = tf.keras.Sequential([
layers.Dense(256, activation='relu'),
layers.Dense(256, activation='relu'),
layers.Dense(1)View Source: https://arxiv.org/abs/2511.16629v1