×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.

Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Click here to flash read.

Reinforcement learning (RL) has proven to be highly effective in tackling
complex decision-making and control tasks. However, prevalent model-free RL
methods often face severe performance degradation due to the well-known
overestimation issue. In response to this problem, we recently introduced an
off-policy RL algorithm, called distributional soft actor-critic (DSAC or
DSAC-v1), which can effectively improve the value estimation accuracy by
learning a continuous Gaussian value distribution. Nonetheless, standard DSAC
has its own shortcomings, including occasionally unstable learning processes
and needs for task-specific reward scaling, which may hinder its overall
performance and adaptability in some special tasks. This paper further
introduces three important refinements to standard DSAC in order to address
these shortcomings. These refinements consist of critic gradient adjusting,
twin value distribution learning, and variance-based target return clipping.
The modified RL algorithm is named as DSAC with three refinements (DSAC-T or
DSAC-v2), and its performances are systematically evaluated on a diverse set of
benchmark tasks. Without any task-specific hyperparameter tuning, DSAC-T
surpasses a lot of mainstream model-free RL algorithms, including SAC, TD3,
DDPG, TRPO, and PPO, in all tested environments. Additionally, DSAC-T, unlike
its standard version, ensures a highly stable learning process and delivers
similar performance across varying reward scales.

Click here to read this post out
ID: 507632; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: Oct. 29, 2023, 7:33 a.m. Changes:
Dictionaries:
Words:
Spaces:
Views: 40
CC:
No creative common's license
Comments: