What is FORK in Actor-Critic Algorithms?
If you're interested in machine learning and artificial intelligence, you might have heard about the term "FORK". But what exactly is FORK and how does it work? In this article, we'll provide an overview of FORK and its role in actor-critic algorithms.
FORK: Forward Looking Actor
FORK stands for Forward Looking Actor, which is a type of actor used in actor-critic algorithms. An actor-critic algorithm is a type of reinforcement learning algorithm where the policy (actor) and value function (critic) are learned simultaneously. The policy learns to select actions that maximize the expected reward, while the value function learns to predict the expected reward for a given state and action.
In traditional actor-critic algorithms, the actor selects actions based on the current state using a policy network, while the critic predicts the expected reward for the current state and action using a value network. However, this approach has some limitations, as it doesn't take into account future states and rewards.
How FORK Works
FORK takes a different approach by using a neural network that can forecast the next state and value of that state when improving the policy. More specifically, FORK includes two neural networks: the system network and the reward network.
The system network takes the current state and action as input and outputs the predicted next state. This allows the actor to take into account the potential future states when selecting actions to maximize the expected reward.
The reward network takes the (state, action) pair as input and outputs the expected reward for that pair. This allows the critic to take into account the potential future rewards when predicting the value of the current state and action.
By using the system network and the reward network, FORK can improve the policy by taking into account the value of the next state and the expected reward for that state. This allows the actor to select actions that not only maximize the expected reward for the current state but also take into account the potential future rewards.
The Advantages of FORK
FORK has several advantages over traditional actor-critic algorithms. First, it can handle non-Markovian environments, where the current state doesn't contain all the information needed to determine the future states and rewards. This is because the system network takes into account the current action and the current state to predict the next state, which can contain more information about the future state than just the current state alone.
Second, FORK can handle delayed rewards, where the reward is not given immediately after taking an action. This is because the reward network takes into account the potential future rewards when predicting the value of the (state, action) pair.
Finally, FORK can handle stochastic environments, where the outcome is not deterministic. This is because the system network can incorporate the uncertainty of the environment by outputting a probability distribution over the possible next states.
Applications of FORK
FORK has been applied in various domains, including robotics, autonomous driving, and game playing. In robotics, FORK has been used to learn policies for locomotion and manipulation tasks, where the robot needs to take into account the dynamics of the environment to accomplish the task.
In autonomous driving, FORK has been used to learn policies for navigating complex environments, where the ego-vehicle needs to take into account the behavior of other vehicles and the uncertainty of the environment.
In game playing, FORK has been used to learn policies for games like Go and Chess, where the agent needs to take into account the potential future states and rewards to make optimal moves.
Overall, FORK is a promising approach to improving actor-critic algorithms by taking into account the potential future states and rewards. By using a system network and a reward network, FORK can handle non-Markovian, delayed, and stochastic environments, which are common in many real-world applications.
As research in machine learning and artificial intelligence advances, we can expect to see more applications of FORK in various domains, leading to more intelligent and capable systems.
