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Xingyuan Zhang

2 accepted papers

2023

Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models

NeurIPS 2023poster

Unlike most reinforcement learning agents which require an unrealistic amount of environment interactions to learn a new behaviour, humans excel at learning quickly by merely observing and imitating others. This ability highly depends on the fact that humans have a model of their own embodiment that…

2022

NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning

NeurIPS 2022accept

Offline reinforcement learning (RL) aims at learning effective policies from historical data without extra environment interactions. During our experience of applying offline RL, we noticed that previous offline RL benchmarks commonly involve significant reality gaps, which we have identified includ…