activity
20202026
most citedA Review of Safe Reinforcement Learning: Methods, Theory and Applications

102 citations · 144 across the 9 of their papers we have counts for

collaborators
Showing 2021Show all

2 papers · 1 filter

cs.AI2021★ 16 cited

Multi-Agent Constrained Policy Optimisation

Shangding Gu, Jakub Grudzien Kuba, Munning Wen +6

Developing reinforcement learning algorithms that satisfy safety constraints is becoming increasingly important in real-world applications. In multi-agent reinforcement learning (M…

cs.LG2021★ 24 cited

Settling the Variance of Multi-Agent Policy Gradients

Jakub Grudzien Kuba, Muning Wen, Yaodong Yang +5

Policy gradient (PG) methods are popular reinforcement learning (RL) methods where a baseline is often applied to reduce the variance of gradient estimates. In multi-agent RL (MARL…