Reinforcement Learning
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization Songtao Lu
Classical bilevel optimization is referred to as the case where there is no consensus constraint but with only two levels of the minimization subproblems, i.e.,
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio
This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions, such that the probability of generating an object is proportional to a given positive reward for that object.
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Equipped with this algorithm, we demonstrate reincarnating RL's gains
Multi-Game Decision Transformers
Transformer models offer the best scalability and performance.
CUP: Critic-Guided Policy Reuse
The ability to reuse previous policies is an important aspect of human intelligence.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
Based on the form of causal knowledge, we divide the related works into two categories, i.e.,
Supplementary Material Policy Learning Using Weak Supervision
In this supplementary material, we first provide theoretical analysis of the convergence rate (Sec A.1) and sample complexity (Sec A.2) for Peer
Policy Learning Using Weak Supervision
Most existing works require agents to receive high-quality supervision signals, e.g., reward or