In Reinforcement Learning, the agent takes actions in the environment, which lead to changes in the state of the environment and the receipt of rewards.

The objective of Reinforcement Learning is to find a policy that maximizes the expected cumulative reward over time.

In a Markov Decision Process, the agent has complete knowledge of the state of the environment at any given time.

The value function of a state is the expected cumulative reward that the agent can obtain by starting from that state and following a given policy.

Policy optimization methods search for a policy that maximizes the expected cumulative reward over time.

Deep Q-Network (DQN) is a deep learning-based Reinforcement Learning algorithm that can handle continuous state and action spaces.

The exploration-exploitation trade-off is the balance between trying new actions to gather information about the environment and sticking to actions that have been successful in the past.

Temporal Difference Learning (TD Learning) is a Reinforcement Learning algorithm that can learn from delayed rewards.

The discount factor is a parameter that determines the importance of future rewards relative to immediate rewards.

Deep Q-Network (DQN) is a deep learning-based Reinforcement Learning algorithm that can learn directly from raw sensory inputs.

Policy evaluation is the process of estimating the value of a given policy, which is the expected cumulative reward that the agent can obtain by following that policy.

Partially Observable Markov Decision Process (POMDP) is a Reinforcement Learning algorithm that can learn in partially observable environments.

The action-value function is a function that estimates the expected cumulative reward for taking a specific action in a given state.

Actor-Critic Methods are Reinforcement Learning algorithms that can learn in continuous state and action spaces without the need for a model of the environment.

Model-based learning is the process of learning a model of the environment and using it to make decisions.