dreamerv2 and dreamer
DreamerV2 is the successor architecture that improves upon the original Dreamer by using discrete latent representations instead of continuous ones, making them sequential versions rather than tools meant to be used together.
About dreamerv2
danijar/dreamerv2
Mastering Atari with Discrete World Models
This project helps reinforcement learning researchers and practitioners train agents that can master complex tasks, particularly in simulated environments like Atari games or robotic control. You provide the environment's visual observations, and it outputs a highly skilled agent capable of achieving human-level or better performance. It's designed for those developing or evaluating advanced AI agents.
About dreamer
danijar/dreamer
Dream to Control: Learning Behaviors by Latent Imagination
This project helps machine learning researchers train agents to perform complex tasks by learning from simulated environments. It takes in observations from a simulated world, like a robot trying to walk, and outputs a control policy that dictates how the agent should behave to achieve its goals. It's designed for reinforcement learning scientists and practitioners developing autonomous agents.
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