# TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning

arXiv: https://arxiv.org/abs/2411.19133 · HTML: https://mlsanity.com/paper/2411.19133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 8 of its 11 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 8 of 8)

- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning](https://mlsanity.com/paper/1910.08348/) (2019)
- [Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning](https://mlsanity.com/paper/2008.00614/) (2020)
- [User-Interactive Offline Reinforcement Learning](https://mlsanity.com/paper/2205.10629/) (2022)
- [Fleet Control using Coregionalized Gaussian Process Policy Iteration](https://mlsanity.com/paper/1911.10121/) (2019)

## Abstract (arXiv)

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.