Reinforcement Learning, Artificial Intelligence, Deep Learning.
state-action pair is visited and then decays over time. The agent updates its policy using the following update rule: θ <- θ + α * e(s, a) * ∇θ Q(s, a) Where α is the learning rate, θ is the parameters of the agent’s policy, e(s, a) is the eligibility trace for the state-action pair, and Q(s, a) is