mlsanityData to 14 Oct 2026

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

arXiv 2411.19133 ↗First posted 28 Nov 2024cs.LGcited by 0

On the map: Offline RL and imitation learning from data → Offline RL algorithms

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.

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.

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8 of 11 references.

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  1. 2022User-Interactive Offline Reinforcement Learning21 May

Other references · 7

  1. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  2. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  3. 2018Recurrent World Models Facilitate Policy Evolutioncited by 785
  4. 2019Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variablescited by 389
  5. 2019VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learningcited by 217
  6. 2020Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learningcited by 26
  7. 2019Fleet Control using Coregionalized Gaussian Process Policy Iterationcited by 5

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  3. 2025Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
  4. 2026MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
  5. 2025Offline Reinforcement Learning with Generative Trajectory Policies
  6. 2023Goal-Conditioned Predictive Coding for Offline Reinforcement Learning
  7. 2022Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
  8. 2022Model-based Trajectory Stitching for Improved Offline Reinforcement Learning