Search Results for author:
Found 11 papers, 0 papers with code
Adaptive Discounting of Training Time Attacks
In this work, we show that a C-TTA is possible even when the target behaviour is un-adoptable due to both environment dynamics as well as non-optimality with respect to the victim objective(s).
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning
This paper proposes such a policy-resilience mechanism based on an idea of knowledge sharing.
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Off-Beat Multi-Agent Reinforcement Learning
During execution durations, the environment changes are influenced by, but not synchronised with, action execution.
Multi-agent Reinforcement Learning
reinforcement-learning
+4
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning
Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning
Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+4
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Under the limited bandwidth constraint, a communication protocol is required to generate informative messages.
Multi-agent Reinforcement Learning
Reinforcement Learning
+1
Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process
We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible.
Security Games with Information Leakage: Modeling and Computation
Our experiments confirm the necessity of handling information leakage and the advantage of our algorithms.
