Learning new task-specific skills from a few trials is a fundamental
cha...
Many practical applications of reinforcement learning require agents to ...
Double Q-learning is a classical method for reducing overestimation bias...
We study deep reinforcement learning (RL) algorithms with delayed reward...
Episodic memory-based methods can rapidly latch onto past successful
str...
We explore value-based multi-agent reinforcement learning (MARL) in the
...
Value decomposition is a popular and promising approach to scaling up
mu...
Goal-oriented reinforcement learning has recently been a practical frame...
Object-based approaches for learning action-conditioned dynamics has
dem...