Stress enhances model-free reinforcement learning only after negative outcome
نویسندگان
چکیده
منابع مشابه
Stress enhances model-free reinforcement learning only after negative outcome
Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the c...
متن کاملReinforcement Learning: Model-free
Simply put, reinforcement learning (RL) is a term used to indicate a large family of dierent 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 کاملMultitask model-free reinforcement learning
Conventional model-free reinforcement learning algorithms are limited to performing only one task, such as navigating to a single goal location in a maze, or reaching one goal state in the Tower of Hanoi block manipulation problem. It has been thought that only model-based algorithms could perform goal-directed actions, optimally adapting to new reward structures in the environment. In this wor...
متن کاملModel-Free Trajectory Optimization for Reinforcement Learning
Many of the recent Trajectory Optimization algorithms alternate between local approximation of the dynamics and conservative policy update. However, linearly approximating the dynamics in order to derive the new policy can bias the update and prevent convergence to the optimal policy. In this article, we propose a new model-free algorithm that backpropagates a local quadratic time-dependent Q-F...
متن کاملModel-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 کاملذخیره 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: PLOS ONE
سال: 2017
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0180588