Continuous action spaces pose a challenge in reinforcement learning because it is not practical to enumerate all possible actions as we can do with discrete action spaces. Instead, we need to explore and optimize policies over a range of continuous actions. Here are three common techniques to handle continuous action spaces in RL:
1. Policy Gradient methods: Policy Gradient methods directly search for a parametric policy in the continuous action space. This enables us to use neural networks to represent policies, which can be trained end-to-end using gradient descent. Some common policy gradient methods are REINFORCE, Actor-Critic, and Proximal Policy Optimization (PPO). These methods update the policy parameters to increase the expected reward obtained by the agent.
2. Value-based methods with continuous action space: Value-based methods like Deep Q-Networks (DQN) focus on learning the optimal action-value function. However, directly applying DQN to continuous action spaces would require sampling actions from a continuous domain, which is inefficient. To overcome this, we can use an actor network that takes the current state as input and outputs the continuous action. This actor network is combined with a value network to form algorithms like Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3).
3. Multinomial methods: Multinomial methods discretize the action space into a finite set of actions by specifying a set of action candidates and then selecting one of them based on a continuous probability distribution. This technique can be used with methods like Q-Learning to extend them to handle continuous action spaces. A popular technique that uses this approach is the Normalized Advantage Function (NAF).
For example, let’s say you are trying to develop a reinforcement learning agent that controls a drone’s flight. The movement of the drone is controlled by continuous action space, such as the amount of thrust provided by the motors. A DQN is not well-suited to handle this continuous action space, so you could use an actor-critic or policy gradient method to directly optimize a policy that maps the state of the drone to the thrust needed for each motor. Alternatively, you could use a value-based method, such as DDPG, that uses an actor network to learn the policy for continuous actions. Finally, you could discretize the drone’s flight domain into a finite set of actions and apply methods like Q-learning to train the agent.