… This paper proposes mixed policy gradient (MPG) algorithm, which fuses the empirical
data and the transition model in policy gradient (PG) to accelerate convergence without …

… family of policy gradient methods that interpolate between on-policy and off-policy learning.
… our interpolated policy gradient method is the use of control variates to mix likelihood ratio …

… gradient updates to consider both an agent’s own non-stationary policy dynamics and the
non-stationary policy … full spectrum of mixed incentive, competitive, and cooperative domains. …

… Multi-agent Deep Deterministic Policy Gradient (M3DDPG) with … policy gradient algorithm
(MADDPG), for robust policy learning; (… We focus on the four mixed cooperative and competitive …

… uses deep deterministic policy gradients to learn policies. However… In addition, FACMAC
uses a centralised policy gradient … mixed settings. We assume each agent a has a deterministic …

… in the existing mixed policy gradient methods, this paper proposes a new mixed policy
gradient form and proposes a novel AC model on the basis of such mixed policy gradient. …

… 4 Mixing-time regularized policy gradient We derive a framework of policy gradient with …
The results in the previous section indicate that, in order to compute some statistics for the policy …

… gradient-based MARL algorithms for identical interest games are quite few. In this article, we
propose a policy gradient … update, as opposed to the gradient itself, to learn the optimal joint …

… We introduce a hybrid policy gradient for deep MARL, known as Q-value Selection using
Optimization and DRL (QSOD), to mitigate this problem. It relies on a grey wolf optimizer (GWO) …

… The blue line separates the data collection and policy gradient training steps in our algorithm
and the … Top: we show the training model where the policy gradient loss is calculated with …