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Found 7 papers, 1 papers with code
Sufficiently Accurate Model Learning for Planning
This paper introduces the constrained Sufficiently Accurate model learning approach, provides examples of such problems, and presents a theorem on how close some approximate solutions can be.
Sufficiently Accurate Model Learning
In this paper, we investigate a method to regularize model learning techniques to provide better error characteristics for traditional control and planning algorithms.
COLLABORATIVE MULTIAGENT REINFORCEMENT LEARNING IN HOMOGENEOUS SWARMS
A deep reinforcement learning solution is developed for a collaborative multiagent system.
Learning Optimal Resource Allocations in Wireless Systems
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints.
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these methods do not scale well.
Distributed Optimization
Multi-agent Reinforcement Learning
+2
Assumed Density Filtering Q-learning
We formulate an efficient closed-form solution for the value update by approximately estimating analytic parameters of the posterior of the Q-beliefs.
Memory Augmented Control Networks
The third part uses a network controller that learns to store those specific instances of past information that are necessary for planning.
