مشاهده مشخصات مقاله
Bayesian Continuous-State Reinforcement Learning
|
نویسنده (ها) |
-
Saeed Amizadeh
-
Majid Nili Ahmadabadi
-
Caro Lucas
|
|
مربوط به کنفرانس |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
|
چکیده |
Continuous-State Reinforcement Learning (RL) has been recently favored because of the continuous nature of
the real world RL problems and many theoretical approaches have been devised to handle the case. However,
most of these methods presume that the structure of the agent's perceptual environment is fed to it. But this is
not the case in many real situations. Inspired from the subjective view existing in the Cognitive Constructivist
learning theory, in this paper, a new method is presented to discover and construct the structure of the
environment in parallel with learning the optimal policy. To achieve these goals, the proposed approach
incorporates the Bayesian formalism to organize the perceptual space while it tries to learn the optimal behavior
using a Q-learning-like learning algorithm. These characteristics as a whole define a Reinforcement Learning
algorithm which is developed based on a mixture of Cognitive Constructivism and traditional Behaviorism ideas.
Simulation results demonstrate the viability and efficiency of the proposed algorithm on continuous state RL
problems. |
|
قیمت |
-
برای اعضای سایت : ۱٠٠,٠٠٠ ریال
-
برای دانشجویان عضو انجمن : ۲٠,٠٠٠ ریال
-
برای اعضای عادی انجمن : ۴٠,٠٠٠ ریال
|
خرید مقاله
|
|
.gif){kind=icon}