Dream to Control: Learning Behaviors by Latent Imagination
While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination.
Dream to Control: Learning Behaviors by Latent Imagination
Dec 3, 2019 · We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model.
[2301.04104] Mastering Diverse Domains through World Models
Jan 10, 2023 · Dreamer learns a model of the environment and improves its behavior by imagining future scenarios. Robustness techniques based on normalization, balancing, and transformations enable stable learning across domains.
Introducing Dreamer: Scalable Reinforcement Learning Using …
Mar 18, 2020 · Today, in collaboration with DeepMind, we present Dreamer, an RL agent that learns a world model from images and uses it to learn long-sighted behaviors. Dreamer leverages its world model to efficiently learn behaviors via backpropagation through model predictions.
GitHub - google-research/dreamer: Dream to Control: Learning …
Dreamer learns a world model from past experience that can predict into the future. It then learns action and value models in its compact latent space. The value model optimizes Bellman consistency of imagined trajectories.
Mastering Diverse Domains through World Models - GitHub
This repository contains a reimplementation of DreamerV3 based on the open source DreamerV2 code base. It is unrelated to Google or DeepMind. The implementation has been tested to reproduce the official results on a range of environments.
Dream to Control: Learning Behaviors by Latent Imagination
While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination.
GitHub - danijar/dreamer: Dream to Control: Learning Behaviors …
Dreamer learns a world model that predicts ahead in a compact feature space. From imagined feature sequences, it learns a policy and state-value function. The value gradients are backpropagated through the multi-step predictions to efficiently learn a long-horizon policy.
We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state
Google-DeepMind’s Dreamer is a Reinforcement Learning Agent …
Jul 27, 2020 · DeepMind and Google recently open sourced Dreamer, a reinforcement learning agent that learns a world model from images and uses it to learn long-sighted behaviors.