no code implementations • 28 Sep 2020 • Justin K. Terry, Nathaniel Grammel, Benjamin Black, Ananth Hari, Caroline Horsch, Luis Santos
Partially Observable Stochastic Games (POSGs) are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL).
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 20 Sep 2020 • Justin K. Terry, Benjamin Black, Luis Santos
The Arcade Learning Environment ("ALE") is a widely used library in the reinforcement learning community that allows easy programmatic interfacing with Atari 2600 games, via the Stella emulator.
1 code implementation • 17 Aug 2020 • Justin K. Terry, Benjamin Black, Ananth Hari
In reinforcement learning, wrappers are universally used to transform the information that passes between a model and an environment.
no code implementations • 11 Jun 2020 • Justin K. Terry, Nathaniel Grammel
We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model.
Multi-agent Reinforcement Learning reinforcement-learning +1
2 code implementations • NeurIPS Workshop ICBINB 2020 • W. Ronny Huang, Zeyad Emam, Micah Goldblum, Liam Fowl, Justin K. Terry, Furong Huang, Tom Goldstein
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.