GenPQR recovers normalized rewards in maximum-entropy IRL by estimating the policy with classification and the soft Q-function with regression, providing modular finite-sample guarantees under general function approximation.
Tree-based batch mode reinforcement learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.
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Inverse Reinforcement Learning with Just Classification and a Few Regressions
GenPQR recovers normalized rewards in maximum-entropy IRL by estimating the policy with classification and the soft Q-function with regression, providing modular finite-sample guarantees under general function approximation.
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Action-Gradient Monte Carlo Tree Search for Non-Parametric Continuous (PO)MDPs
AGMCTS augments MCTS with action-score gradients for particle beliefs, a Multiple Importance Sampling tree for reuse, and Area Formula gradients for smooth models, outperforming prior sample-based solvers on continuous benchmarks.