Search Results for author: Keyang He

Found 4 papers, 0 papers with code

Latent Interactive A2C for Improved RL in Open Many-Agent Systems

no code implementations9 May 2023 Keyang He, Prashant Doshi, Bikramjit Banerjee

There is a prevalence of multiagent reinforcement learning (MARL) methods that engage in centralized training.

Many Agent Reinforcement Learning Under Partial Observability

no code implementations17 Jun 2021 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-critic architectures, and can be applied to a limited range of settings in terms of observability and communication.

Multi-agent Reinforcement Learning reinforcement-learning +1

Reinforcement Learning for Heterogeneous Teams with PALO Bounds

no code implementations23 May 2018 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, those shared by all agents in the domain.

reinforcement-learning Reinforcement Learning (RL)

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