What Are Some of the Most Used Reinforcement Learning Algorithms?

Tiara Williamson
Tiara WilliamsonAnswered

There is a subfield of machine learning called reinforcement learning (RL) that emphasizes discovery via trial-and-error interactions with the surrounding environment. Many well-known methods of RL are implemented to teach agents how to maximize rewards in different settings. Popular basic machine learning algorithms include:

Q-Learning and Deep Q-Networks

Q-Learning is a model-free, off-policy method that learns the best policy by consulting a table of Q-values. The Q-values are adjusted upward or downward depending on the discrepancy between expected and actual payoffs.

Deep Q-Networks (DQN) use deep neural networks to approximate the Q-values. The training process is stabilized with the help of experience replay and a target network.


As a model-based algorithm, Actor-Critic integrates value-based and policy-based approaches. It employs a critic network to predict the worth of activities and an actor network to find the optimal course of action.

Policy Gradient and Proximal Policy Optimization

In order to train a policy function directly, Policy Gradient employs gradient ascent without the need for a model. It does so by readjusting the policy parameters according to the predicted reward’s gradient with respect to those settings.

PPO, or proximal policy optimization, is an on-policy approach that does not need a model and adjusts the policy’s parameters based on a clipped surrogate objective function. The policy adjustments are kept to a minimum to maintain or increase stability and convergence.


TRPO is a model-free, on-policy algorithm that uses a trust region optimization technique to fine-tune the policy’s settings. In order to maintain stability and progress toward a common goal, this method limits policy shifts within a manageable range.

SARSA (State-Action-Reward-State-Action) is a model-free, on-policy method that adjusts Q-values according to the SARSA tuple. By making decisions depending on the existing policy, it is able to learn.

There are pros and cons to each of the most popular RL algorithms. Whatever algorithm is used to complete a job is conditional on the nature of the issue at hand.

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