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pro vyhledávání: '"Wang, Woodrow Z."'
Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to
Externí odkaz:
http://arxiv.org/abs/2110.08229
Autor:
Wang, Woodrow Z., Beliaev, Mark, Bıyık, Erdem, Lazar, Daniel A., Pedarsani, Ramtin, Sadigh, Dorsa
Coordination is often critical to forming prosocial behaviors -- behaviors that increase the overall sum of rewards received by all agents in a multi-agent game. However, state of the art reinforcement learning algorithms often suffer from converging
Externí odkaz:
http://arxiv.org/abs/2105.06593
Autor:
Beliaev, Mark, Bıyık, Erdem, Lazar, Daniel A., Wang, Woodrow Z., Sadigh, Dorsa, Pedarsani, Ramtin
The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a re-opening stage, some effects of the pandemic on people's behaviors are expected to last much longer, including how they choose between d
Externí odkaz:
http://arxiv.org/abs/2012.15749
Autor:
Cao, Zhangjie, Bıyık, Erdem, Wang, Woodrow Z., Raventos, Allan, Gaidon, Adrien, Rosman, Guy, Sadigh, Dorsa
Autonomous driving has achieved significant progress in recent years, but autonomous cars are still unable to tackle high-risk situations where a potential accident is likely. In such near-accident scenarios, even a minor change in the vehicle's acti
Externí odkaz:
http://arxiv.org/abs/2007.00178