Model-based policy optimization with unsupervised model adaptation
Abstract Model-based reinforcement learning methods learn a dynamics model with real
data sampled from the environment and leverage it to generate simulated data to derive an …
data sampled from the environment and leverage it to generate simulated data to derive an …
Adaptive rollout length for model-based rl using model-free deep rl
Model-based reinforcement learning promises to learn an optimal policy from fewer
interactions with the environment compared to model-free reinforcement learning by …
interactions with the environment compared to model-free reinforcement learning by …
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts
Y Pan, F Lin - 2022 IEEE/RSJ International Conference on …, 2022 - ieeexplore.ieee.org
Traditional model-based reinforcement learning (RL) methods generate forward rollout
traces using the learnt dynamics model to reduce interactions with the real environment. The …
traces using the learnt dynamics model to reduce interactions with the real environment. The …