by combining it with uncertainty estimation and imitation), how to construct deep learning architectures that give agents the right abilities (such as long-term memory), and how to enable agents that can rapidly adapt to new game situations. Below,...
A Virtual Maze Game to Explain Reinforcement LearningYouri CoppensEugenio BargiacchiAnn NowéCEUR Workshop ProceedingsBelgium-Netherlands Conference on Artificial Intelligence
Another example of reinforcement learning can be found when playing the game Go. If the computer player puts down its white piece at a location, then gets surrounded by the black pieces and loses that space, it is punished for taking such a move. After being beaten a few times, the compu...
In a maze game, a good action is where the agent makes a move so that it doesn't hit a maze wall, whereas a bad action is where the agent moves and hits the maze wall. An agent tries to maximize the total amount of rewards (cumulative rewards) it receives from the environment inste...
They’ve developed tools allowing people to experiment with the technology, including a demo allowing people to play a simple game with a reinforcement learning agent to see how it reacts as well as Azure Machine Learningsample notebooksto create an agent that can navigate a lava maze in Minecr...
This culminates in the development of a Reachable Coverage-based Hierarchical Reinforcement Learning method (RCHRL). Experimental validation in complex robot control tasks and Maze environments demonstrates the efficacy of our approach in removing demonstration noises, surpassing recent state-of-the-art ...
In fact, in some cases the agent may know everything about how its environment works and still face a difficult reinforcement learning task, just as we may know exactly how a puzzle like Rubik’s cube works, but still be unable to solve it.(稀疏奖励) ...
4.1 Autoregressive Discrete Q-Learning 将Transformer与Q学习结合带来了两个挑战：我们必须将输入token化...
s capabilities. Then we quickly revisit the maze game fromChapter 3and show you how to tackle it both with the RLlib CLI and the RLlib Python API in a few lines of code. You’ll see how easy RLlib is to get started before learning about its key concepts, such as RLlib environments ...
Reinforcement learning is a core technology for modern artificial intelligence, and it has become a workhorse for AI applications ranging from Atrai Game to Connected and Automated Vehicle System (CAV). Therefore, a reliable RL system is the foundation for the security critical applications in AI, ...