In this article, we have covered 50+ Key Terms in the domain of Reinforcement Learning. This will give a strong hold on RL.

What is RL?

Reinforcement learning (RL) is a type of machine learning where an agent learns to interact with an environment by taking actions and receiving feedback in the form of rewards or penalties. The goal of RL is to find an optimal policy, which is a mapping from states to actions that determines the behavior of the agent in the environment. The agent learns from experiences by updating its policy based on the rewards it receives, with the objective of maximizing the cumulative reward over time. There are several key terms in RL that are important to understand. Let's take a look at some of them:

Key terms in RL

Agent

The entity that learns to interact with the environment by taking actions and receiving rewards or penalties.

Environment

The world in which the agent operates and receives feedback.

State

A representation of the environment that summarizes the information needed to make decisions.

Action

A decision made by the agent in a particular state.

Policy

A policy is a mapping from states to actions. It determines the behavior of an agent in the environment. The goal of RL is to find an optimal policy that maximizes the cumulative reward over time.

On-policy

On-policy methods update the policy that is used to interact with the environment. This means that the agent learns from the experiences it has while following its current policy.

Off-policy

Off-policy methods update a different policy than the one used to interact with the environment. This means that the agent learns from experiences generated by a different behavior policy.

Exploration vs. exploitation

Exploration is the process of trying out different actions in order to learn more about the environment.
Exploitation is the process of choosing the action that is believed to be the best based on the current knowledge. Balancing exploration and exploitation is a key challenge in RL.

Epsilon-greedy policy

Epsilon-greedy policy is a common exploration strategy in RL. It chooses the best action with probability (1 - epsilon) and a random action with probability epsilon. The value of epsilon determines the amount of exploration.

Q-value

The Q-value of a state-action pair is the expected cumulative reward that can be obtained by starting from that state, taking that action, and following a given policy. Q-values are used to evaluate and improve policies.

Value function

The value function of a state is the expected cumulative reward that can be obtained by starting from that state and following a given policy. Value functions are used to evaluate and improve policies.

Bellman equation

The Bellman equation is a recursive equation that relates the value of a state to the values of its successor states. It is used to update value functions in RL.

Temporal difference learning

Temporal difference (TD) learning is a type of RL algorithm that updates value functions by using the difference between the predicted and actual rewards.

Model-based vs. model-free

Model-based RL methods learn a model of the environment (e.g., transition probabilities) and use it to make decisions.
Model-free methods do not rely on a model and instead learn directly from experience.

Reward function

The reward function is a function that maps states and actions to numerical rewards. It defines the goal of the RL problem and is used to guide the agent towards achieving that goal.

Discount factor

The discount factor is a parameter that determines the importance of future rewards in the RL problem. A high discount factor means that future rewards are given more importance, while a low discount factor means that immediate rewards are given more importance.

Monte Carlo methods

Monte Carlo methods are a type of RL algorithm that estimate value functions by averaging the actual rewards obtained from a sequence of actions. They do not rely on a model of the environment.

Policy gradient methods

Policy gradient methods are a type of RL algorithm that directly optimize the policy by computing gradients of the expected reward with respect to the policy parameters.

Actor-critic methods

Actor-critic methods are a type of RL algorithm that combines the advantages of both policy gradient and value-based methods. They use a separate critic to estimate value functions and an actor to learn the policy.

Deep reinforcement learning

Deep reinforcement learning refers to the use of deep neural networks to approximate value functions or policies in RL problems. It has been shown to be effective in learning complex tasks.

Exploration strategies

Exploration strategies are methods for encouraging the agent to explore the environment in order to find the optimal policy. These can include epsilon-greedy, softmax, and Thompson sampling.

Replay buffer

A replay buffer is a memory structure used in RL algorithms to store experiences (i.e., state, action, reward, next state) for later use in training.

Transfer learning

Transfer learning is the process of transferring knowledge learned from one RL task to another related task in order to speed up learning.

Curriculum learning

Curriculum learning is the process of gradually increasing the difficulty of the RL task by starting with simple tasks and gradually moving towards more complex tasks. This can speed up learning and prevent the agent from getting stuck in suboptimal solutions.

Bandits and types

A type of RL problem in which the agent must choose from a set of actions with unknown reward probabilities, and the goal is to maximize the cumulative reward over time. There are two types of bandit problems: stochastic and adversarial.

Episode

A sequence of states, actions, and rewards that starts with an initial state and ends with a terminal state.

Markov Decision Process (MDP)

A mathematical framework for modeling sequential decision-making problems in which the environment is fully observable and the future outcomes depend only on the current state and action.

Deep Q-Networks (DQN)

A type of RL algorithm that uses a neural network to approximate the action-value function in a Q-learning algorithm.

Terminal state

A state in which the episode ends and no further rewards are received.

Upper Confidence Bound (UCB)

A method for balancing exploration and exploitation in multi-armed bandit problems by selecting actions with high expected reward and high uncertainty.

Proximal Policy Optimization (PPO)

A type of policy gradient algorithm that maximizes the objective function while keeping the updated policy close to the old policy to ensure stability.

REINFORCE

A classic policy gradient algorithm that updates the policy parameters in the direction of the gradient of the expected reward with respect to the policy parameters.

Exploration-Exploitation Dilemma

The trade-off between exploring new actions with unknown rewards and exploiting the current knowledge to maximize the cumulative reward.

Deadly Triad Issue

A phenomenon in which the combination of function approximation, bootstrapping, and off-policy updates in RL algorithms can lead to instability and divergence.

Conclusion

These are some of the key terms in reinforcement learning that are important to understand. By mastering these concepts mentioned in this article at OpenGenus, you will be better equipped to design, implement, and optimize RL algorithms for a wide range of applications.