DreamerV2 reaches human-level performance on 55 Atari games by learning behaviors inside a separately trained discrete-latent world model.
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
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Dreamer learns to control from images by imagining and optimizing behaviors in a learned latent world model, outperforming prior methods on 20 visual tasks in data efficiency and final performance.
MSDP pre-trains a transformer encoder with masked multisensory autoencoding, then uses an asymmetric actor-critic bridge (cross-attention for critic, pooling for actor) to accelerate and robustify contact-rich RL across simulation and real robots.
citing papers explorer
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Mastering Atari with Discrete World Models
DreamerV2 reaches human-level performance on 55 Atari games by learning behaviors inside a separately trained discrete-latent world model.
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Dream to Control: Learning Behaviors by Latent Imagination
Dreamer learns to control from images by imagining and optimizing behaviors in a learned latent world model, outperforming prior methods on 20 visual tasks in data efficiency and final performance.
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Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning
MSDP pre-trains a transformer encoder with masked multisensory autoencoding, then uses an asymmetric actor-critic bridge (cross-attention for critic, pooling for actor) to accelerate and robustify contact-rich RL across simulation and real robots.