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CAST: Alternating State-Value Targets and Expanded Policy Gradients for Model-Based Reinforcement Learning

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Model-based reinforcement learning (MBRL) is a family of RL methods that learn a model of the environment and use it for action selection, making it well suited to robotics due to its sample efficiency. Combining learned models with online planning can further improve action selection, as the planner can exploit the model to find better actions than the learned policy alone. Recent methods combining learned policies with online planning typically learn the value of the policy rather than the stronger planner-guided behavior. We present CAST (Critic with Alternating State-value Target), which u

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