Model-based and model-free reinforcement learning: the experiments

نویسندگان

چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Learning Epistemic Actions in Model-Free Memory-Free Reinforcement Learning: Experiments with a Neuro-robotic Model

Passive sensory processing is often insufficient to guide biological organisms in complex environments. Rather, behaviourally relevant information can be accessed by performing so-called epistemic actions that explicitly aim at unveiling hidden information. However, it is still unclear how an autonomous agent can learn epistemic actions and how it can use them adaptively. In this work, we propo...

متن کامل

Reinforcement Learning: Model-free

Simply put, reinforcement learning (RL) is a term used to indicate a large family of dierent algorithms RL that all share two key properties. First, the objective of RL is to learn appropriate behavior through trialand-error experience in a task. Second, in RL, the feedback available to the learning agent is restricted to a reward signal that indicates how well the agent is behaving, but does ...

متن کامل

Model-Free Preference-Based Reinforcement Learning

Specifying a numeric reward function for reinforcement learning typically requires a lot of hand-tuning from a human expert. In contrast, preference-based reinforcement learning (PBRL) utilizes only pairwise comparisons between trajectories as a feedback signal, which are often more intuitive to specify. Currently available approaches to PBRL for control problems with continuous state/action sp...

متن کامل

MBMF: Model-Based Priors for Model-Free Reinforcement Learning

Reinforcement Learning is divided in two main paradigms: model-free and model-based. Each of these two paradigms has strengths and limitations, and has been successfully applied to real world domains that are appropriate to its corresponding strengths. In this paper, we present a new approach aimed at bridging the gap between these two paradigms. We aim to take the best of the two paradigms and...

متن کامل

Shaping Model-Free Reinforcement Learning with Model- Based Pseudorewards

Model-free and model-based reinforcement learning have provided a successful framework for understanding both human behavior and neural data. These two systems are usually thought to compete for control of behavior. However, it has also been proposed that they can be integrated in a cooperative manner. For example, the Dyna algorithm uses model-based replay of past experience to train the model...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Frontiers in Computational Neuroscience

سال: 2011

ISSN: 1662-5188

DOI: 10.3389/conf.fncom.2011.53.00019