Search Results for author:
Found 3 papers, 3 papers with code
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
Unity: A General Platform for Intelligent Agents
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments.
