3 citations · 4 across the 3 of their papers we have counts for
5 papers · 1 filter
Generative Actor-Critic with Soft Bridge Policies
Ke He, Le He, Shunpu Tang +2
Expressive generative policies such as diffusion and flow models are appealing for MaxEnt online reinforcement learning because of their ability to model multimodal and highly non-…
Latent Interactive A2C for Improved RL in Open Many-Agent Systems
Keyang He, Prashant Doshi, Bikramjit Banerjee
There is a prevalence of multiagent reinforcement learning (MARL) methods that engage in centralized training. But, these methods involve obtaining various types of information fro…
Many Agent Reinforcement Learning Under Partial Observability
Keyang He, Prashant Doshi, Bikramjit Banerjee
Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an impressive array of techniques that leverage deep reinforcement learning, primarily actor-crit…
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards
Keyang He, Bikramjit Banerjee, Prashant Doshi
Consider a typical organization whose worker agents seek to collectively cooperate for its general betterment. However, each individual agent simultaneously seeks to act to secure…
Reinforcement Learning for Heterogeneous Teams with PALO Bounds
Roi Ceren, Prashant Doshi, Keyang He
We introduce reinforcement learning for heterogeneous teams in which rewards for an agent are additively factored into local costs, stimuli unique to each agent, and global rewards…