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Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning
In response to this gap, we adapt the celebrated Nesterov's accelerated gradient (NAG) method to policy optimization in RL, termed \textit{Accelerated Policy Gradient} (APG).
Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network
This model architecture aims to capture the computations in V1-MT, the core structure for motion perception in the biological visual system, while providing the ability to derive informative motion flow for a wide range of stimuli, including complex natural scenes.
Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient.
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