Search Results for author:
Found 36 papers, 14 papers with code
Online Planning in POMDPs with State-Requests
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereby compelling the agent to operate under partial observability.
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning
Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs).
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
We evaluated MO-DCMAC using two utility functions, which use probability of collapse and cost as input.
Multi-Objective Reinforcement Learning
reinforcement-learning
Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning
A significant challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies that attain optimal performance under different preferences.
Multi-Objective Reinforcement Learning
reinforcement-learning
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning
reinforcement-learning
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms.
Distributional Multi-Objective Decision Making
For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker.
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable.
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning.
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning
Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.
Multi-Objective Reinforcement Learning
reinforcement-learning
+1
Determining Accessible Sidewalk Width by Extracting Obstacle Information from Point Clouds
Obstacles on the sidewalk often block the path, limiting passage and resulting in frustration and wasted time, especially for citizens and visitors who use assistive devices (wheelchairs, walkers, strollers, canes, etc).
Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models
In such settings a set of optimal policies must be computed.
Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning
As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning
Many recent successful off-policy multi-agent reinforcement learning (MARL) algorithms for cooperative partially observable environments focus on finding factorized value functions, leading to convoluted network structures.
Preference Communication in Multi-Objective Normal-Form Games
We consider preference communication in two-player multi-objective normal-form games.
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making
In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised.
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy.
Scalable Optimization for Wind Farm Control using Coordination Graphs
This is a non-trivial optimization problem, as complex dependencies exist between the wind turbines.
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i. e., learning while considering the impact of one's policy when anticipating the opponent's learning step).
Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
Evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time.
Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
We generalize the well-studied problem of gait learning in modular robots in two dimensions.
A utility-based analysis of equilibria in multi-objective normal form games
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions.
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes.
Model-based Reinforcement Learning
Multi-agent Reinforcement Learning
+3
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
In this task, wind turbines must coordinate their alignments with respect to the incoming wind vector in order to optimize power production.
Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey
We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied.
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
We argue that actor-critic algorithms are limited by their need for an on-policy critic.
The Actor-Advisor: Policy Gradient With Off-Policy Advice
In this paper, we propose an elegant solution, the Actor-Advisor architecture, in which a Policy Gradient actor learns from unbiased Monte-Carlo returns, while being shaped (or advised) by the Softmax policy arising from an off-policy critic.
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required.
Multi-Objective Reinforcement Learning
reinforcement-learning
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster.
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering.
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i. e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature.
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability.
Multi-Objective Deep Reinforcement Learning
We propose Deep Optimistic Linear Support Learning (DOL) to solve high-dimensional multi-objective decision problems where the relative importances of the objectives are not known a priori.
Multi-Objective Reinforcement Learning
reinforcement-learning
Structure in the Value Function of Two-Player Zero-Sum Games of Incomplete Information
Zero-sum stochastic games provide a rich model for competitive decision making.
Solving Transition-Independent Multi-agent MDPs with Sparse Interactions (Extended version)
In cooperative multi-agent sequential decision making under uncertainty, agents must coordinate to find an optimal joint policy that maximises joint value.
