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cs.LG2024
Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
Erin J. Talvitie, Zilei Shao, Huiying Li +4
In model-based reinforcement learning, simulated experiences from the learned model are often treated as equivalent to experience from the real environment. However, when the model…
cs.LG2020
Selective Dyna-style Planning Under Limited Model Capacity
Zaheer Abbas, Samuel Sokota, Erin J. Talvitie +1
In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may s…
cs.LG2020
Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Models
Farzane Aminmansour, Taher Jafferjee, Ehsan Imani +3
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environme…