4 papers
Neuro-symbolic Action Masking for Deep Reinforcement Learning
Shuai Han, Mehdi Dastani, Shihan Wang
Deep reinforcement learning (DRL) may explore infeasible actions during training and execution. Existing approaches assume a symbol grounding function that maps high-dimensional st…
Learning Communication Skills in Multi-task Multi-agent Deep Reinforcement Learning
Changxi Zhu, Mehdi Dastani, Shihan Wang
In multi-agent deep reinforcement learning (MADRL), agents can communicate with one another to perform a task in a coordinated manner. When multiple tasks are involved, agents can…
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning
Shuai Han, Mehdi Dastani, Shihan Wang
Training cooperative agents in sparse-reward scenarios poses significant challenges for multi-agent reinforcement learning (MARL). Without clear feedback on actions at each step in…
Reducing Variance Caused by Communication in Decentralized Multi-agent Deep Reinforcement Learning
Changxi Zhu, Mehdi Dastani, Shihan Wang
In decentralized multi-agent deep reinforcement learning (MADRL), communication can help agents to gain a better understanding of the environment to better coordinate their behavio…