Jun 21, 2014 · In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing …

Deep Deterministic Policy Gradient (DDPG) is an algorithm which concurrently learns a Q-function and a policy. It uses off-policy data and the Bellman equation to learn the Q-function, and uses …

We use the deterministic policy gradient to derive an off-policy actor-critic algorithm that estimates the action-value function us-ing a differentiable function approximator, and then up-dates the …

May 15, 2024 · Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based …

Aug 26, 2021 · Deterministic Policy Gradient Algorithms. With the deterministic policy gradient, we can derive different kinds of algorithms such as Actor-Critic methods for both on-policy and off …

Apr 22, 2025 · Reinforcement learning algorithms that handle continuous action spaces have the problem of slow convergence and local optimality. Hence, we propose a deep deterministic …

Deep Deterministic Policy Gradient (DDPG) is an advanced algorithm used in reinforcement learning (RL) to train agents in continuous action spaces. RL is a type of machine learning …

Apr 19, 2024 · Deep Deterministic Policy Gradient (DDPG) is an algorithm that combines Policy Gradient and Q-Learning. The DDPG algorithm is described below. 1. Initialization: Initialize …

Since deterministic policy regularization is impossible using traditional non-metric measures such as the KL divergence, we derive a Wasserstein-based quadratic model for our purposes. We …

Why do we care about Policy Gradient (PG)?