Search Results for author:
Found 2 papers, 1 papers with code
Efficient Reinforcement Learning Development with RLzoo
RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).
Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning
In this method, iterative update can greatly alleviate the nonstationarity of the environment, unified representation can speed up the interaction with environment and avoid the linear growth of memory usage.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
