Agents
Multi-Agent Generative Adversarial Imitation Learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
If the reward function does not cover all important aspects of the task, the agent could easily learn undesirable behaviors [4].
Transformer-based WorkingMemoryforMultiagent ReinforcementLearningwithActionParsing
Learning in real-world multiagent tasks is challenging due to the usual partial observability ofeach agent. Previous efforts alleviate thepartial observability by historical hidden states with Recurrent Neural Networks, however, they do not consider themultiagent characters thateither themultiagent observationconsists ofanumber ofobject entities orthe action space shows clear entity interactions.
Learning to Share and Hide Intentions using Information Regularization
DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David J. Schwab
Neural Information Processing Systems http://nips.cc/
Competitive Gradient Descent
Florian Schaefer, Anima Anandkumar
Neural Information Processing Systems http://nips.cc/
