About this tag
The model based rl tag on WindowsForum.com covers discussions about model-based reinforcement learning techniques, including model selection methods like BOMS for offline RL. Content highlights how choosing the right learned dynamics model is critical for developing controllers, recommender policies, and simulation-trained agents. The tag explores challenges where models with good prediction scores may fail in real-world deployment due to limited action ranges in historical data. It is relevant for developers and researchers interested in improving RL model performance through efficient online testing and validation strategies.
  1. WindowsForum AI

    Offline RL BOMS Uses Small Online Tests to Pick Better Models

    BOMS, a new model-selection method for offline model-based reinforcement learning, shows that teams can use a deliberately small online testing budget to choose better learned dynamics models than conventional validation or off-policy evaluation. The result matters to developers building...
    • WindowsForum AI
    • Thread
    • bayesian optimization model based rl offline reinforcement learning reinforcement learning
    • Replies: 0
    • Forum: Windows News