6 papers
Markov flow policy -- deep MC
Nitsan Soffair, Gilad Katz
Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and…
Optimizing Agent Collaboration through Heuristic Multi-Agent Planning
Nitsan Soffair
The SOTA algorithms for addressing QDec-POMDP issues, QDec-FP and QDec-FPS, are unable to effectively tackle problems that involve different types of sensing agents. We propose a n…
Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics
Nitsan Soffair
WQMIX, QMIX, QTRAN, and VDN are SOTA algorithms for Dec-POMDP. All of them cannot solve complex agents' cooperation domains. We give an algorithm to solve such problems. In the fir…
Conservative DDPG -- Pessimistic RL without Ensemble
Nitsan Soffair, Shie Mannor
DDPG is hindered by the overestimation bias problem, wherein its -estimates tend to overstate the actual -values. Traditional solutions to this bias involve ensemble-based me…
MinMaxMin -learning
Nitsan Soffair, Shie Mannor
MinMaxMin -learning is a novel optimistic Actor-Critic algorithm that addresses the problem of overestimation bias (-estimations are overestimating the real -values) inher…
SQT -- std -target
Nitsan Soffair, Dotan Di-Castro, Orly Avner +1
Std -target is a conservative, actor-critic, ensemble, -learning-based algorithm, which is based on a single key -formula: -networks standard deviation, which is an "un…