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Found 32 papers, 4 papers with code
Neural Approximate Dynamic Programming for On-Demand Ride-Pooling
This is because even a myopic assignment in ride-pooling involves considering what combinations of passenger requests that can be assigned to vehicles, which adds a layer of combinatorial complexity to the ToD problem.
Generative Modelling of Stochastic Actions with Arbitrary Constraints in Reinforcement Learning
Many problems in Reinforcement Learning (RL) seek an optimal policy with large discrete multidimensional yet unordered action spaces; these include problems in randomized allocation of resources such as placements of multiple security resources and emergency response units, etc.
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement Learning
In an exhaustive set of experiments, we demonstrate that our approach is able to outperform top benchmark approaches for solving Constrained RL problems, with respect to expected cost, CVaR cost, or even unknown cost constraints.
Imitating Cost-Constrained Behaviors in Reinforcement Learning
Generally speaking, imitation learning is designed to learn either the reward (or preference) model or directly the behavioral policy by observing the behavior of an expert.
Entropy based Independent Learning in Anonymous Multi-Agent Settings
A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i. e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents.
Resource Constrained Deep Reinforcement Learning
However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources.
TuSeRACT: Turn-Sample-Based Real-Time Traffic Signal Control
To ensure real-time responsiveness in the presence of turn-induced uncertainty, SURTRAC computes schedules which minimize the delay for the expected turn movements as opposed to minimizing the expected delay under turn-induced uncertainty.
Regret based Robust Solutions for Uncertain Markov Decision Processes
Most robust optimization approaches for these problems have focussed on the computation of {\em maximin} policies which maximize the value corresponding to the worst realization of the uncertainty.
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning
To solve the online problem with a hard bound on risk, we formulate it as a Reinforcement Learning (RL) problem with constraints on the action space (hard bound on risk).
On Solving Cooperative MARL Problems with a Few Good Experiences
Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively.
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems
For effective matching of resources (e. g., taxis, food, bikes, shopping items) to customer demand, aggregation systems have been extremely successful.
Zone pAth Construction (ZAC) based Approaches for Effective Real-Time Ridesharing
This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible in real-time; and then (ii) optimizing assignment of the feasible request combinations to vehicles.
Competitive Ratios for Online Multi-capacity Ridesharing
The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i. e., a request can be part of at most one request group in the final assignment).
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100, 000 live births.
Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information.
Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study.
Facilitating human-wildlife cohabitation through conflict prediction
With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic).
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling
Owing to the benefits for customers (lower prices), drivers (higher revenues), aggregation companies (higher revenues) and the environment (fewer vehicles), on-demand ride pooling (e. g., Uber pool, Grab Share) has become quite popular.
Efficient Resource Allocation with Fairness Constraints in Restless Multi-Armed Bandits
In this paper, we are interested in ensuring that RMAB decision making is also fair to different arms while maximizing expected value.
Towards Soft Fairness in Restless Multi-Armed Bandits
To avoid starvation in the executed interventions across individuals/regions/communities, we first provide a soft fairness constraint and then provide an approach to enforce the soft fairness constraint in RMABs.
Learning Individual Policies in Large Multi-agent Systems through Local Variance Minimization
In multi-agent systems with large number of agents, typically the contribution of each agent to the value of other agents is minimal (e. g., aggregation systems such as Uber, Deliveroo).
Generalization through Diversity: Improving Unsupervised Environment Design
Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e. g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board).
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties
Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints.
Diversity Induced Environment Design via Self-Play
In this paper, we aim to introduce diversity in the Unsupervised Environment Design (UED) framework.
Regret-Based Defense in Adversarial Reinforcement Learning
We demonstrate that our approaches provide a significant improvement in performance across a wide variety of benchmarks against leading approaches for robust Deep RL.
Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks.
Future Aware Pricing and Matching for Sustainable On-demand Ride Pooling
Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests.
Transferable Curricula through Difficulty Conditioned Generators
In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents in parameterized environments.
Enhancing the Hierarchical Environment Design via Generative Trajectory Modeling
Unsupervised Environment Design (UED) is a paradigm for automatically generating a curriculum of training environments, enabling agents trained in these environments to develop general capabilities, i. e., achieving good zero-shot transfer performance.
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators
We adapt Parameterized Environment Response Model (PERM), a method for training both Reinforcement Learning (RL) Agents and human learners in parameterized environments by directly modeling difficulty and ability.
SubIQ: Inverse Soft-Q Learning for Offline Imitation with Suboptimal Demonstrations
Most of the existing offline IL methods developed for this setting are based on behavior cloning or distribution matching, where the aim is to match the occupancy distribution of the imitation policy with that of the expert policy.
