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Found 3 papers, 1 papers with code
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment.
Extracting Strong Policies for Robotics Tasks from zero-order trajectory optimizers
Solving high-dimensional, continuous robotic tasks is a challenging optimization problem.
Sample-efficient Cross-Entropy Method for Real-time Planning
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning
reinforcement-learning
+1
