Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. The goal of RL is to find an optimal policy, mapping states of the environment to actions, that maximizes the cumulative reward over time.
RL algorithms differ from supervised and unsupervised learning algorithms in that they do not have labeled training data, but instead learn from trial and error. The agent receives feedback in the form of a reward signal, indicating how well it is doing with respect to its goals.
RL can be modeled as a Markov Decision Process (MDP), where the system is described by a set of states, actions, and transition probabilities between states given an action. The reward signal is defined as a function that maps states and actions to a scalar reward.
The core idea behind RL is to estimate the optimal value function, which represents the expected cumulative reward an agent can receive by following a certain policy. The value function can be estimated using several algorithms, including Dynamic Programming, Monte Carlo methods, and Temporal-Difference (TD) learning.
One popular TD learning algorithm is Q-Learning. In Q-Learning, the agent learns an estimate of the optimal action-value function, denoted as Q(s, a), which represents the expected cumulative reward of taking action a in state s and following the optimal policy thereafter. The algorithm iteratively updates the Q-values based on observed transitions and rewards, until it converges to an optimal estimate.
Another popular algorithm is SARSA (State-Action-Reward-State-Action), which is similar to Q-Learning but estimates the expected cumulative reward of taking action a in state s and following the same policy thereafter.
Actor-Critic algorithms, such as A2C (Advantage Actor-Critic) and PPO (Proximal Policy Optimization), are a class of RL algorithms that combine both policy-based and value-based methods. The Actor network is responsible for choosing actions based on the current state, while the Critic network estimates the value function, providing a baseline for the Actor to improve its policy.
Deep Reinforcement Learning (DRL) is a variant of RL that uses deep neural networks as function approximators for the value or policy functions. DRL has been applied to a wide range of challenging problems, such as playing video games, robotics, and autonomous driving.
DRL algorithms can be further divided into two categories: value-based methods and policy-based methods. Value-based methods, such as Deep Q-Networks (DQN), use a deep neural network to estimate the Q-values. Policy-based methods, such as REINFORCE, use a deep neural network to directly parameterize the policy.
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. RL algorithms can be modeled as a Markov Decision Process and the core idea is to estimate the optimal value function. TD learning algorithms, such as Q-Learning and SARSA, and Actor-Critic algorithms, such as A2C and PPO, are popular RL algorithms. DRL is a variant of RL that uses deep neural networks as function approximators for the value or policy functions.