S2P: State-conditioned Image Synthesis For Data Augmentation In Offline Reinforcement Learning

Abstract

Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limitation, state-based offline RL leverages a learned dynamics model from the logged experience and augments the predicted state transition to extend the data distribution. For exploiting such benefit also on the image-based RL, we firstly propose a generative model, S2P (State2Pixel), which synthesizes the raw pixel of the agent from its corresponding state. It enables bridging the gap between the state and the image domain in RL algorithms, and virtually exploring unseen image distribution via model-based transition in the state space. Through experiments, we confirm that our S2P-based image synthesis not only improves the image-based offline RL performance but also shows powerful generalization capability on unseen tasks.

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keycho2022s2p

Related papers

• Exploiting Generalization In Offline Reinforcement Learning Via Unseen State Augmentations (2023)0.00
• State-constrained Offline Reinforcement Learning (2024)0.00
• A Policy-guided Imitation Approach For Offline Reinforcement Learning (2022)0.00
• Boosting Offline Reinforcement Learning With Residual Generative Modeling (2021)0.00
• Goal-conditioned Data Augmentation For Offline Reinforcement Learning (2024)0.00
• Equivariant Data Augmentation For Generalization In Offline Reinforcement Learning (2023)0.00
• Goal-conditioned Offline Reinforcement Learning Through State Space Partitioning (2023)2.26
• Decision Mamba: A Multi-grained State Space Model With Self-evolution Regularization For Offline RL (2024)0.00