… reinforcement learning from a computer-science perspective. It is written to be accessible to
researchers familiar with machine learning. … model-free algorithms for reinforcement learning …

… These studies show that efficient exploration can be achieved using the reward function
estimated by inverse reinforcement learning if we can prepare expert’s behaviors. …

… model-based reinforcement learning and their relation to the fields of planning and model-free
reinforcement learning (… of most model-based RL algorithms is dynamics model learning (…

… A model in reinforcement learning 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 …

… exciting achievements of deep reinforcement learning (RL). … machine learning, deep learning
and reinforcement learning. … (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 …

… of selfimproving and online learning make reinforcement learning become one of … model
and theory of reinforcement learning. Then, we roundly present the main reinforcement learning …

… Reinforcement learning models have recently started to be … from the use of reinforcement
learning models. We close by … specifics of how reinforcement learning models provide insights …

… However, some of the best model-free reinforcement learning 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 reinforcement learning … the
model. Then this policy is deployed in the real system. Research in reinforcement learning and …