This paper proposes a Regularly Updated Deterministic (RUD) policy gradient algorithm for these problems. This paper theoretically proves that the learning procedure with RUD can make better use of new data in replay buffer than the traditional procedure. In addition, the low variance of the Q ...
[15]Deterministic Policy Gradient Algorithms, Silver et al, 2014.Algorithm: DPG. 背景介绍 Deterministic Policy是相对于Stochastic Policy而言的。其中Stochastic Policy的表达式为 πθ(a|s)=P[a|s;θ] ,在实际应用中,大家往往采用高斯分布来作为策略的分布。其中高斯分布的均值和方差都由神经网络来近似。 而...
该定理将动作价值的梯度用策略函数的梯度和参数w的线性组合表示,进一步提出了a compatible off-policy deterministic actor-critic algorithm以及在critic中应用了gradient temporal-difference learning。这部分具体的细节没有去推导。 Experiment: 第一个实验在continous bandit问题上进行了随机策略梯度和确定性策略梯度的比较...
(exploiting the ef-,ciency of the deterministic policy gradient).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 policy parameters in the direction ...
This simple form means that the deter-ministic policy gradient can be estimated muchmore efficiently than the usual stochastic pol-icy gradient. To ensure adequate exploration,we introduce an off-policy actor-critic algorithmthat learns a deterministic target policy from anexploratory behaviour policy....
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 the Q-function to learn the policy. This approach is closely connected to Q-learning, and is mo...
Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are ...
The deep deterministic policy gradient (DDPG) algorithm is an off-policy actor-critic method for environments with a continuous action-space. A DDPG agent learns a deterministic policy while also using a Q-value function critic to estimate the value of the optimal policy. It features a target ...
(2023). MADDPG: Multi-agent Deep Deterministic Policy Gradient Algorithm for Formation Elliptical Encirclement and Collision Avoidance. In: Ren, Z., Wang, M., Hua, Y. (eds) Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Lecture Notes in Electrical ...
Discussion and Related Work Using a stochastic policy gradient algorithm, the policy becomes more deterministic as the algorithm homes in on a good strategy. Unfortunately this makes the stochastic policy gradient harder to estimate, because the policy gradient ?θπθ (a|s) changes more rapidly ...