Understanding N-Step Returns in Reinforcement Learning
Reinforcement learning is about teaching machines to learn and improve how they perform certain tasks. One of the techniques used in reinforcement learning is the use of value functions. Value functions help algorithms determine the best actions to take for each state in a particular environment. Value functions are estimates of how good a specific state or action is for a machine or agent. However, estimating value functions is often chall
Retrace is a Q-value estimation algorithm used in reinforcement learning. It works best when there are two policies, a target policy and a behavior policy, denoted as $\pi$ and $\beta$, respectively. The algorithm uses off-policy rollout for TD learning, meaning that it uses data generated by following one policy while trying to learn about another policy.
Importance Sampling
In Retrace, importance sampling is used for the update of Q-values. Importance sampling is a technique used in statist
What is a Stochastic Dueling Network?
A Stochastic Dueling Network, or SDN, is a type of machine learning architecture used to learn a value function called V. Essentially, it is a way for a computer program to estimate the value of possible actions in a given situation.
The way an SDN works is that it uses two models that work together: a stochastic model and a deterministic model. The deterministic model estimates the value of each possible action, while the stochastic model estimates the pr
Reinforcement learning is the process of an artificial intelligence (AI) learning through trial and error. One of the algorithms used in reinforcement learning is V-trace.
What is V-trace?
V-trace is an off-policy actor-critic reinforcement learning algorithm. It helps tackle the lag between when actions are generated by the actors and when the learner estimates the gradient. The algorithm is used to learn policies that maximize the expected reward that the AI will receive over time.
The V-t