Actor-critic methods
Actor-critic methods are a family of reinforcement learning algorithms that learn two things at once: a parameterized policy, called the actor, which chooses actions, and a value function, called the critic
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Matrix multiplication
Matrix multiplication combines an M by K matrix A with a K by N matrix B to produce an M by N matrix C
Soft Actor-Critic
Soft Actor-Critic (SAC) is an off-policy, maximum-entropy deep reinforcement learning algorithm that trains a stochastic actor-critic to maximize expected reward plus the entropy of its own policy, so the…
Twin Delayed DDPG
Twin Delayed Deep Deterministic Policy Gradient (TD3) is an off-policy actor-critic reinforcement learning algorithm for continuous action spaces, introduced by Scott Fujimoto, Herke van Hoof, and David Meger…