… These studies show that efficient exploration can be achieved using the reward function estimated by inverse reinforcementlearning if we can prepare expert’s behaviors. …
… model-based reinforcementlearning and their relation to the fields of planning and model-free reinforcementlearning (… of most model-based RL algorithms is dynamics modellearning (…
RS Sutton, AG Barto - Journal of Cognitive Neuroscience, 1999 - academia.edu
… A model in reinforcementlearning is a representation of the environment, which is used for … use of models as part of the agent’s state space, the reinforcement learning approaches in …
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
… exciting achievements of deep reinforcement learning (RL). … machine learning, deep learning and reinforcementlearning. … (DQN), policy, reward, model and planning, exploration, and …
… model-free approaches. On the other hand, planning algorithms that can make use of a learned model of the environment belong to the so-called model-… a model-based or a model-free …
W Qiang, Z Zhongli - 2011 International Conference on …, 2011 - ieeexplore.ieee.org
… of selfimproving and online learning make reinforcementlearning become one of … model and theory of reinforcementlearning. Then, we roundly present the main reinforcementlearning …
… Reinforcementlearningmodels have recently started to be … from the use of reinforcement learningmodels. We close by … specifics of how reinforcementlearningmodels provide insights …
… However, some of the best model-free reinforcementlearning algorithms require tens or … In this paper, we explore how learned video models can enable learning in the Atari Learning …
… conference on Machine learning: Using inaccurate models in reinforcementlearning … the model. Then this policy is deployed in the real system. Research in reinforcementlearning and …