Gato is a multi-modal, multi-task, multi-embodiment generalist policy using one transformer network to handle text, vision, games, and robotics tasks.
arXiv preprint arXiv:2104.06159 , year=
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RAT estimates Tikhonov-regularized natural policy gradients by rewriting them with the Woodbury identity, approximating the transformed advantage via randomized block Kaczmarz, and applying it as a vanilla policy gradient surrogate.
The paper introduces ANPS and SV-PPO to enable larger target policy updates in deep RL by approximating the next policy's visitation distribution during value function training.
citing papers explorer
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A Generalist Agent
Gato is a multi-modal, multi-task, multi-embodiment generalist policy using one transformer network to handle text, vision, games, and robotics tasks.
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Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation
RAT estimates Tikhonov-regularized natural policy gradients by rewriting them with the Woodbury identity, approximating the transformed advantage via randomized block Kaczmarz, and applying it as a vanilla policy gradient surrogate.
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Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
The paper introduces ANPS and SV-PPO to enable larger target policy updates in deep RL by approximating the next policy's visitation distribution during value function training.