Search Results for author:
Found 3 papers, 0 papers with code
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization
Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many concerns.
Solving single-objective tasks by preference multi-objective reinforcement learning
We analyzed the feasibility of our algorithm in theory, and further proved in experiments its better performance compared to those that design the reward function by experts.
Multi-Objective Reinforcement Learning
reinforcement-learning
Abstraction Learning
Specifically, we propose a partition structure that contains pre-allocated abstraction neurons; we formulate abstraction learning as a constrained optimization problem, which integrates abstraction properties; we develop a network evolution algorithm to solve this problem.
