Domain transfer methods aim to bridge this dynamics gap through techniques such as domain adaptation or domain calibration.

Both challenges involve modeling long-term dependencies.

In a standard view of the reinforcement learning problem, an agent's goal is to efficiently identify a policy that maximizes long-term reward.

Reinforcement learning with the help of neural-guided search consumes huge computational resources to achieve remarkable performance.

We consider the problem of extracting a reward function by comparing a decision-making diffusion model that models low-reward behavior and one that models high-reward behavior; a setting related to inverse reinforcement learning. We first define the notion of a relative reward function of two diffusion models and show conditions under which it exists and is unique.

In this paper, we propose a provably efficient natural policy gradient algorithm called Spectral Dynamic Embedding Policy Optimization ( SDEPO) for two-player zero-sum stochastic Markov games with continuous state space and finite action space. In the policy evaluation procedure of our algorithm, a novel kernel embedding method is employed to construct a finite-dimensional linear approximations to the state-action value function.

Many articles in the literature solve the CMDP with an unknown environment.