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Found 10 papers, 0 papers with code
Adversarially Trained Actor Critic for offline CMDPs
Theoretically, we demonstrate that when the actor employs a no-regret optimization oracle, SATAC achieves two guarantees: (i) For the first time in the offline RL setting, we establish that SATAC can produce a policy that outperforms the behavior policy while maintaining the same level of safety, which is critical to designing an algorithm for offline RL.
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Fairness plays a crucial role in various multi-agent systems (e. g., communication networks, financial markets, etc.).
Provably Efficient Model-Free Algorithms for Non-stationary CMDPs
We study model-free reinforcement learning (RL) algorithms in episodic non-stationary constrained Markov Decision Processes (CMDPs), in which an agent aims to maximize the expected cumulative reward subject to a cumulative constraint on the expected utility (cost).
Provably Efficient Model-free RL in Leader-Follower MDP with Linear Function Approximation
We focus on a setup where both the leader and followers are {\em non-myopic}, i. e., they both seek to maximize their rewards over the entire episode and consider a linear MDP which can model continuous state-space which is very common in many RL applications.
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
To this end, we consider the episodic constrained Markov decision processes with linear function approximation, where the transition dynamics and the reward function can be represented as a linear function of some known feature mapping.
Traffic Control in a Mixed Autonomy Scenario at Urban Intersections: An Optimization-based Framework
We consider an intersection zone where autonomous vehicles (AVs) and human-driven vehicles (HDVs) can be present.
Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach
We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks.
Model Free Reinforcement Learning Algorithm for Stationary Mean field Equilibrium for Multiple Types of Agents
We consider a multi-agent Markov strategic interaction over an infinite horizon where agents can be of multiple types.
Reinforcement Learning for Mean Field Game
This paper focuses on finding a mean-field equilibrium (MFE) in an action coupled stochastic game setting in an episodic framework.
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
The success of modern ride-sharing platforms crucially depends on the profit of the ride-sharing fleet operating companies, and how efficiently the resources are managed.
