PG-DPO is a new variational framework that replaces Bellman recursion with a Pontryagin-guided adjoint-MC projection for RL under non-exponential discounting and shows gains on hyperbolic and survival benchmarks.
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Creativity is defined as meta-learning where a frozen diffusion creator optimizes candidates for rapid improvement by an adapting appraiser such as an autoencoder or CLIP adapter.
New concentration bounds and stopping rule close the exponential gap to match the lower bound for entropic best policy identification.
Aggregation distorts parametric behavioral curve peaks by factors of 3-5x via Simpson's paradox and survival bias, shown by individual vs. aggregate comparisons on Goodreads and Amazon datasets with a negative control.
Derives contraction-based Q-value extensions for exponential utility and proves almost-sure convergence of two-timescale and one-timescale model-free algorithms in discounted MDPs.
LIMEN discovers effective RL interfaces by using LLMs to evolve observation and reward programs together from raw state, guided by policy training success, outperforming single-component optimization.
AEL uses a fast-timescale bandit for memory policy selection and slow-timescale LLM reflection for causal insights, achieving a Sharpe ratio of 2.13 on a 208-episode portfolio benchmark while showing that added mechanisms degrade performance.
An infra-Bayesian RL agent is implemented that achieves lower worst-case regret than classical RL agents in environments with Knightian uncertainty and selects the optimal action in Newcomb's problem.
Reinforcement learning with graph neural networks finds minimally rigid graphs that match known planar realization optima and set new records for spherical realization counts.
RLBD trains a neural policy with REINFORCE to select cuts adaptively in Benders decomposition, yielding faster convergence and better generalization than standard BD or SVM-based LearnBD on an EV charging problem.
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
Develops greedy optimization algorithms for directly learning optimal integer-weighted clinical risk scores, applied to predict post-discharge mortality in a large EHR cohort with a supporting simulation study.
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