TL;DR
This work proposes Continuous Nested Monte Carlo Search and Continuous Nested Rollout Policy Adaptation and shows that they provide promising results on three problems: Map Traveling, Mountain Car Continuous and two instances of Chemical Processes from the pc-gym package.
A new robust variant of MCTS that mitigates dynamical model ambiguities to bridge the gap between simulation-based planning and real-world deployment and empirical evidence is provided that this method achieves robust performance in planning problems even under significant ambiguity in the underlying reward distributio...
Reinforcement learning in real-life applications requires safety guarantees during exploration. Typical reinforcement learning algorithms do not provide such guarantees, and many modifications that do rely on Gaussian processes (GPs), which have a large computational cost. We propose a computationally lightweight algor...
GEM-MPC is proposed, an MPPI-based reinforcement learning method that improves the interaction between planning and learning and selectively learns from stored planning distributions only when they provide a better target than the current prior, reducing the impact of stale planning data without requiring full reanalys...
This note argues that, at the level of trajectory generation and action-value updating, the distinction between Monte Carlo Tree Search and MC control is largely terminological.
Extensive simulations and real-world experiments demonstrate that the proposed framework can efficiently generate and iteratively improve motion plans for different planning objectives, robotic platforms, and swarm configurations, highlighting its effectiveness, computational efficiency, and scalability as a general pl...
A navigation policy in which a convolutional network computes a shared spatial field over the map once per timestep and a compact goal-conditioned operator derives agents'movement preferences is proposed.
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