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Found 3 papers, 1 papers with code
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)
Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments.
A Risk-Sensitive Approach to Policy Optimization
Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation.
On the Complexity of Reconnaissance Blind Chess
This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent's pieces a priori but may reveal a subset of them through chosen, private sensing actions.
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