Paper ID: 2411.19133 • Published Nov 28, 2024
TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning
Batıkan Bora Ormancı, Phillip Swazinna, Steffen Udluft, Thomas A. Runkler
TL;DR
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In this paper, we investigate offline reinforcement learning (RL) with the
goal of training a single robust policy that generalizes effectively across
environments with unseen dynamics. We propose a novel approach, Trajectory
Encoding Augmentation (TEA), which extends the state space by integrating
latent representations of environmental dynamics obtained from sequence
encoders, such as AutoEncoders. Our findings show that incorporating these
encodings with TEA improves the transferability of a single policy to novel
environments with new dynamics, surpassing methods that rely solely on
unmodified states. These results indicate that TEA captures critical,
environment-specific characteristics, enabling RL agents to generalize
effectively across dynamic conditions.