Zero-shot Policy Learning With Spatial Temporal Rewarddecomposition On Contingency-aware Observation

Abstract

It is a long-standing challenge to enable an intelligent agent to learn in one environment and generalize to an unseen environment without further data collection and finetuning. In this paper, we consider a zero shot generalization problem setup that complies with biological intelligent agents' learning and generalization processes. The agent is first presented with previous experiences in the training environment, along with task description in the form of trajectory-level sparse rewards. Later when it is placed in the new testing environment, it is asked to perform the task without any interaction with the testing environment. We find this setting natural for biological creatures and at the same time, challenging for previous methods. Behavior cloning, state-of-art RL along with other zero-shot learning methods perform poorly on this benchmark. Given a set of experiences in the training environment, our method learns a neural function that decomposes the sparse reward into particula

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyxu2019zero

Related papers

• Zero-shot Generalization Of Vision-based RL Without Data Augmentation (2024)0.00
• Cross-trajectory Representation Learning For Zero-shot Generalization In RL (2021)0.00
• A Unified Framework For Zero-shot Reinforcement Learning (2025)0.00
• Inferring Behavior-specific Context Improves Zero-shot Generalization In Reinforcement Learning (2024)0.95
• On Zero-shot Reinforcement Learning (2025)0.00
• Learning Long-term Reward Redistribution Via Randomized Return Decomposition (2021)0.00
• Inverse Reinforcement Learning From Non-stationary Learning Agents (2024)0.00
• DRED: Zero-shot Transfer In Reinforcement Learning Via Data-regularised Environment Design (2024)1.81