Types of Reinforcement Learning

Depending on how agents makes its decisions, they can be classified into one of the following types:

Value-Based: In value-based reinforcement learning, the agent learns to predict the expected reward for each action in a given state. The agent then chooses the action that leads to the highest expected reward.
Policy-Based: In policy-based reinforcement learning, the agent learns to map states directly to actions without computing a value function. The agent improves its policy through trial and error by taking actions and receiving rewards.
Model-Based: In model-based reinforcement learning, the agent builds a model of the environment that predicts the next state and reward for a given action. The agent uses this model to plan its actions and improve its policy.
Actor-Critic: Actor-critic is a combination of value-based and policy-based reinforcement learning. The actor learns the policy that maps states to actions, while the critic learns the value function that evaluates the expected reward for a given state and action. These are the main types of reinforcement learning, each with its own strengths and weaknesses. The choice of which type to use depends on the specific problem and the requirements of the task at hand.

reward penalty