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Found 4 papers, 4 papers with code
Policy-Guided Diffusion
Our approach provides an effective alternative to autoregressive offline world models, opening the door to the controllable generation of synthetic training data.
Discovering Temporally-Aware Reinforcement Learning Algorithms
We propose a simple augmentation to two existing objective discovery approaches that allows the discovered algorithm to dynamically update its objective function throughout the agent's training procedure, resulting in expressive schedules and increased generalization across different training horizons.
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
Hypernetworks in Meta-Reinforcement Learning
In this paper, we 1) show that hypernetwork initialization is also a critical factor in meta-RL, and that naive initializations yield poor performance; 2) propose a novel hypernetwork initialization scheme that matches or exceeds the performance of a state-of-the-art approach proposed for supervised settings, as well as being simpler and more general; and 3) use this method to show that hypernetworks can improve performance in meta-RL by evaluating on multiple simulated robotics benchmarks.
