Well File:
- Well Planning ( results)
- Shallow Hazard Analysis ( results)
- Well Plat ( results)
- Wellbore Schematic ( results)
- Directional Survey ( results)
- Fluid Sample ( results)
- Log ( results)
- Density ( results)
- Gamma Ray ( results)
- Mud ( results)
- Resistivity ( results)
- Report ( results)
- Daily Report ( results)
- End of Well Report ( results)
- Well Completion Report ( results)
- Rock Sample ( results)
Jack Serrino
Finding Friend and Foe in Multi-Agent Games
Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum
Neural Information Processing Systems http://nips.cc/
Finding Friend and Foe in Multi-Agent Games
Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Josh Tenenbaum
Recent breakthroughs in AI for multi-agent games like Go, Poker, and Dota, have seen great strides in recent years. Yet none of these games address the real-life challenge of cooperation in the presence of unknown and uncertain teammates. This challenge is a key game mechanism in hidden role games. Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game. DeepRole combines counterfactual regret minimization (CFR) with deep value networks trained through self-play.