research
∙
07/04/2023
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offlin...
share
research
∙
05/26/2023
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Combinatorial optimization (CO) problems are often NP-hard and thus out ...
research
∙
02/19/2023
Stochastic Generative Flow Networks
Generative Flow Networks (or GFlowNets for short) are a family of probab...
research
∙
02/11/2023
Distributional GFlowNets with Quantile Flows
Generative Flow Networks (GFlowNets) are a new family of probabilistic s...
research
∙
02/03/2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov ...
research
∙
10/07/2022
Generative Augmented Flow Networks
The Generative Flow Network is a probabilistic framework where an agent ...
research
∙
08/30/2022
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Multi-user delay constrained scheduling is important in many real-world ...
research
∙
05/30/2022
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Training deep reinforcement learning (DRL) models usually requires high ...
research
∙
04/19/2022
Network Topology Optimization via Deep Reinforcement Learning
Topology impacts important network performance metrics, including link u...
research
∙
11/22/2021
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
The idea of conservatism has led to significant progress in offline rein...
research
∙
03/22/2021
Regularized Softmax Deep Multi-Agent Q-Learning
Tackling overestimation in Q-learning is an important problem that has b...
research
∙
10/19/2020
Softmax Deep Double Deterministic Policy Gradients
A widely-used actor-critic reinforcement learning algorithm for continuo...
research
∙
11/11/2019
Multi-Path Policy Optimization
Recent years have witnessed a tremendous improvement of deep reinforceme...
research
∙
09/09/2019
Deterministic Value-Policy Gradients
Reinforcement learning algorithms such as the deep deterministic policy ...
research
∙
03/14/2019
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
Value function estimation is an important task in reinforcement learning...
research
∙
07/10/2018
Generalized deterministic policy gradient algorithms
We study a setting of reinforcement learning, where the state transition...
research
∙
02/13/2018