Multi-objective Gymnasium environments for reinforcement learning
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Updated
Aug 19, 2026 - Python
Multi-objective Gymnasium environments for reinforcement learning
Multi-Objective Reinforcement Learning algorithms implementations.
Extended, multi-agent, and multi-objective (MaMoRL / MoMaRL) gridworld environments building framework based on DeepMind's AI Safety Gridworlds. This is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. It is made compatible with OpenAI's Gym/Gymnasium and Farama Foundation PettingZoo.
Safety challenges for RL and LLM agents' ability to learn and properly apply biologically and economically aligned utility functions. The benchmarks are implemented in a gridworld-based environment. The environments are relatively simple, just as much complexity is added as is necessary to illustrate the relevant safety and performance aspects.
Robotics: Science and Systems (RSS) 2026 | PASTA 🍝 | Stable Policy Optimization for Non-Convex Pareto Tradeoffs.
[NeurIPS 2021] Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning
Multi-Objective Multi-Agent RL with non-linear utility functions
A multi-objective reinforcement learning system based on Pareto Q-Learning to optimize traffic light control in SUMO, balancing travel time, emissions, and traffic flow.
Preference-conditioned MORL for runtime-tunable transit signal priority in SUMO/IntersectionZoo.
risk-sensitive multi-objective RL (KR-IQN) in ICML 2026
Readme for Biological and Economical Alignment Benchmarks
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