<ul data-eligibleForWebStory="true"><li>Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms aim to enhance sample efficiency by generating synthetic state transition data.</li><li>A study found a performance gap in DMBRL algorithms when applied to different benchmark environments with proprioceptive observations.</li><li>While DMBRL algorithms perform well in OpenAI Gym, their performance drops significantly in DeepMind Control Suite (DMC) despite similar tasks and physics backends.</li><li>Modern techniques addressing key issues in these settings do not consistently improve performance across all environments.</li><li>Adding synthetic rollouts to the training process, a core aspect of Dyna-style algorithms, actually degrades performance in most DMC environments.</li><li>The study sheds light on challenges in model-based RL and highlights that there is no 'free lunch' when evaluating performance across various benchmarks in RL.</li></ul>

Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning

Discover more