Markov Models
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning Benjamin Ellis 1 Jonathan Cook
We show that these changes ensure the benchmark requires the use of closed-loop policies.
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics
Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities.
RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization
Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.
OFCOURSE: A Multi-Agent Reinforcement Learning Environment for Order Fulfillment
In particular, we model the integrated problem as a Markov game, wherein a team of agents learns a joint policy via interacting with a simulated environment.
Probabilistic Inference in Reinforcement Learning Done Right Jean T arbouriech Google DeepMind
A popular perspective in Reinforcement learning (RL) casts the problem as probabilistic inference on a graphical model of the Markov decision process (MDP). The core object of study is the probability of each state-action pair being visited under the optimal policy.
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation
This paper investigates posterior sampling algorithms for competitive reinforcement learning (RL) in the context of general function approximations.