3 articles tagged with #reward-machines. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce coupled reward machines (CRMs) and the QCoRM algorithm to improve reinforcement learning efficiency for long-horizon tasks with unordered subtasks. The approach scales exponentially better than existing methods by using compact reward representations and task decomposition, with validation across discrete and continuous environments.
Researchers introduce QR-MAX, a model-based reinforcement learning algorithm designed for non-Markovian reward decision processes that depend on complete system history rather than current state alone. The algorithm provides formal PAC convergence guarantees with polynomial sample complexity, advancing a previously under-theorized area of RL with practical applications to temporal-dependency tasks.
Researchers propose Symbolic Reward Machines (SRMs) as an improvement over traditional Reward Machines in reinforcement learning, eliminating the need for manual user input while maintaining performance. SRMs process observations directly through symbolic formulas, making them more applicable to widely adopted RL frameworks.
