Deep Reinforcement Learning incorporates deep neural networks into the framework of Reinforcement Learning. This integration allows for more complex and high-dimensional representations of states and actions, enabling the agent to learn and make decisions in environments with large state spaces. In traditional Reinforcement Learning approaches, such as Q-Learning, a tabular…

Policy-Based Methods In policy-based methods, the focus is on directly learning the policy function, which maps states to the best corresponding actions. The policy can be deterministic, meaning it always selects the same action for a given state, or stochastic, where it outputs a probability distribution over the set of actions for…

The exploration/exploitation trade-off is a fundamental concept in reinforcement learning that refers to the dilemma of choosing between exploring new possibilities and exploiting known knowledge to maximize rewards. Exploration involves taking actions that are uncertain or unknown to gather more information about the environment. It allows the agent to discover…

Episodic Task In an episodic task, the problem has a well-defined starting point and an ending point, also known as a terminal state. Each episode consists of a sequence of states, actions, rewards, and new states that occur from the start to the end. The agent’s goal is to maximize the cumulative…

In reinforcement learning (RL), the reward serves as the fundamental feedback for the agent’s actions. It is through the reward that the agent learns whether the action taken was good or not, as the reward indicates the desirability of the outcome. Positive rewards reinforce actions that lead to favorable outcomes…

Observations: Observations refer to the information that an agent receives from the environment. In the context of reinforcement learning, observations are typically used to capture the current state or partial information about the state of the environment. Observations can take various forms depending on the specific problem or domain. For…

The Markov Property in Markov Decision Processes (MDPs) is a fundamental concept that significantly impacts the agent’s decision-making process. This property states that the agent’s decisions depend solely on the current state, disregarding the entire history of past states and actions. …

In reinforcement learning, the learning process typically follows a loop that generates a sequence of state-action-reward-next state tuples. This sequence is often referred to as an “experience replay” or “trajectory.” In reinforcement learning, the agent’s ultimate goal is to maximize its cumulative reward, which is often referred to as the…

The agent receives the initial state, denoted as S₀, from the environment. In this case, the state represents the first frame of a game. Based on the received state S₀, the agent takes an action A₀. In this example, the agent decides to move to the right. The environment reacts…