Multi-Objective Reinforcement Learning
32 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Objective Reinforcement Learning
Libraries
Use these libraries to find Multi-Objective Reinforcement Learning models and implementationsMost implemented papers
Optimization of Molecules via Deep Reinforcement Learning
google-research/google-research
•
•
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).
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.
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation
RunzheYang/MORL
•
•
NeurIPS 2019
We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks.
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.
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as proportional fairness (smooth concave scalarization), hard constraints (constrained MDP), and max-min trade-off.
MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments.
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
lucasalegre/morl-baselines
•
•
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.
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
lucasalegre/morl-baselines
•
•
Conference on Neural Information Processing Systems Datasets and Benchmarks Track 2023
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function.
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
A Gaussian process is used to obtain a smooth interpolation over the reward function weights of the optimal value function for three well-known examples: GridWorld, Objectworld and Pendulum.
A Distributional View on Multi-Objective Policy Optimization
Many real-world problems require trading off multiple competing objectives.
