Researchers have introduced Pareto Q-Learning with Reward Machines (PQLRM), a novel multi-objective reinforcement learning algorithm designed for tasks with complex reward structures defined by reward machines. This algorithm integrates Pareto Q-Learning, which handles vector-valued Q-estimates for Pareto front approximation, with enhancements from Q-Learning with Reward Machines that leverage the automaton structure of reward signals. PQLRM aims to achieve sample efficiency in non-Markovian, reward machine-encoded environments and has demonstrated faster convergence and the ability to synthesize Pareto-optimal policies that other methods cannot. AI
IMPACT Enhances sample efficiency and policy synthesis in multi-objective reinforcement learning tasks with complex reward structures.
RANK_REASON The cluster contains a research paper submitted to arXiv detailing a new algorithm in reinforcement learning.
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- Influence Flower
- Markov decision process
- Pareto Q-Learning
- Pareto Q-Learning with Reward Machines
- Q-Learning with Reward Machines
- Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
- ScienceCast
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