Search Results for author:
Found 5 papers, 3 papers with code
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+3
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
As the complexity of CPS evolved, the focus has shifted from traditional control methods to deep reinforcement learning-based (DRL) methods for control of these systems.
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
While robust control has a long history of development, robust ML is an emerging research area that has already demonstrated its relevance and urgency.
Deep Reinforcement Learning for Adaptive Traffic Signal Control
In this paper, we propose a DRL-based adaptive traffic signal control framework that explicitly considers realistic traffic scenarios, sensors, and physical constraints.
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
In this work, we first frame the problem as an optimization problem of minimizing the cumulative reward of an RL agent with decoupled constraints as the budget of attack.
