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)
Hoang Le
Imitation-Projected Programmatic Reinforcement Learning
Abhinav Verma, Hoang Le, Yisong Yue, Swarat Chaudhuri
Neural Information Processing Systems http://nips.cc/
Imitation-Projected Programmatic Reinforcement Learning
Abhinav Verma, Hoang Le, Yisong Yue, Swarat Chaudhuri
We study the problem of programmatic reinforcement learning, in which policies are represented as short programs in a symbolic language. Programmatic policies can be more interpretable, generalizable, and amenable to formal verification than neural policies; however, designing rigorous learning approaches for such policies remains a challenge.