View RSS Feed

mql5

Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization

Rate this Entry
by , 04-28-2024 at 04:31 PM (276 Views)
      
   
Reinforcement learning is a universal platform for learning optimal behavior policies in the environment under exploration. Policy optimality is achieved by maximizing the rewards received from the environment during interaction with it. But herein lies one of the main problems of this approach. The creation of an appropriate reward function often requires significant human effort. Additionally, rewards may be sparse and/or insufficient to express the true learning goal. As one of the options for solving this problem, the authors if the paper "Beyond Reward: Offline Preference-guided Policy Optimization" suggested the OPPO method (OPPO stands for the Offline Preference-guided Policy Optimization). The authors of the method suggest the replacement of the reward given by the environment with the preferences of the human annotator between two trajectories completed in the environment under exploration. Let's take a closer look at the proposed algorithm.
more...

Submit "Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization" to Google Submit "Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization" to del.icio.us Submit "Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization" to Digg Submit "Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization" to reddit

Categories
Uncategorized

Comments