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What is Model-free reinforcement learning
Exploring Model-Free Reinforcement Learning
Introduction
Reinforcement Learning (RL) is a subfield of Machine Learning (ML) that focuses on developing algorithms for agents to learn by interacting with the environment. The RL-based agents learn through trial and error and feedback from the environment, with the goal of maximizing the rewards received. Model-Free Reinforcement Learning is one approach of RL that doesn't take into account the model of the environment, i.e., how the environment works. Instead, it tries to learn directly from experience.
Model-Free RL is a powerful approach as it is well-suited for dynamic and complex environments where the model of the environment is either unknown or hard to compute. In this article, let's explore Model-Free RL, its types, and how it works.
Types of Model-Free RL
Model-Free RL can be broadly classified into two types, namely,
- Value-Based RL
- Policy-Based RL
Let's discuss each of these types in detail.
Value-Based RL
Value-Based RL is a type of Model-Free RL that learns the value of each state or action in the environment, i.e., how good/bad a certain state or action is. The value function of a state is the expected total reward that can be earned from that state onwards. The algorithms involved in Value-Based RL are primarily concerned with estimating the optimal value function, which leads to the best possible policy. Some of the popular algorithms involved in Value-Based RL are,
- Q-Learning
- SARSA (State-Action-Reward-State-Action)
- Expected SARSA
- Deep Q-Network (DQN)
Q-Learning is perhaps the most popular and commonly used Value-Based RL algorithm. It works by estimating the Q-Values for each state-action pair, which indicates the maximum cumulative discounted return that can be obtained if that action is taken from that state onwards. SARSA is another popular algorithm that learns the Q-Value function by following an on-policy approach, where the agent learns the Q-Values for the policy it is following. Expected SARSA, on the other hand, is an off-policy algorithm that learns the Q-Values for a completely different policy by taking the expected value of the Q-Values for all possible actions from a state. Finally, DQN is a deep learning-based variant of Q-Learning that uses neural networks to learn the Q-Values.
Policy-Based RL
Policy-Based RL is another popular type of Model-Free RL that focuses on learning the optimal policy that maximizes the total reward. The algorithms involved in Policy-Based RL learn a policy that maps states to actions and optimize the policy parameters using some optimization method like gradient descent. Some of the popular algorithms in Policy-Based RL are,
- Policy Gradient Methods
- Actor-Critic Methods
- Deep Deterministic Policy Gradient (DDPG)
Policy Gradient Methods learn a policy by directly optimizing it using the gradient of the expected return with respect to the policy parameters. Actor-Critic Methods, on the other hand, combine the strengths of both value-based and policy-based methods by using two models, an Actor model that learns the policy and a Critic model that learns the value function. Finally, DDPG is a deep learning-based variant of Actor-Critic Methods that uses neural networks to learn the policy and value functions.
How Model-Free RL Works
Model-Free RL algorithms learn by updating their estimates of the value function based on the feedback received from the environment in the form of rewards. The agent interacts with the environment, observes the state it is in, takes an action, observes a new state and the corresponding reward, and updates its estimate of the value function based on these observations. The agent then uses its updated value function to determine the best action to take in the next time step.
As mentioned earlier, Value-Based RL learns the value function, while Policy-Based RL learns the optimal policy. In other words, Value-Based RL algorithms learn an optimal Q-Value function, while Policy-Based algorithms learn an optimal policy function. These functions are learned through repeated episodes of interacting with the environment and updating the function estimates based on the observed state, action, reward, and new state pairs.
Advantages and Disadvantages of Model-Free RL
Model-Free RL has several advantages, some of which are,
- Doesn't require knowledge of the model of the environment to learn
- Well-suited for complex and dynamic environments
- Can handle continuous and high-dimensional state/action spaces
- Can learn online, i.e., in real-time as the agent interacts with the environment
However, Model-Free RL also has some disadvantages, which are,
- Can take a long time to converge to an optimal policy or value function estimate
- Can suffer from high variance in the learning process
- Can be sensitive to the choice of hyperparameters and tuning them can be time-consuming
- Can suffer from exploration-exploitation trade-offs
Conclusion
Model-Free RL is a popular approach in the field of Reinforcement Learning that doesn't require knowledge of the model of the environment to learn. It can handle complex and dynamic environments and has several advantages. However, it also has some disadvantages, which need to be considered while choosing a suitable RL algorithm depending on the application. The choice between Value-Based and Policy-Based RL algorithms depends on the application requirements and design. Overall, Model-Free RL has shown promising results in various domains like robotics, gaming, and control systems, and is a highly active area of research in ML and AI.