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Found 3 papers, 1 papers with code
Planning with General Objective Functions: Going Beyond Total Rewards
Standard sequential decision-making paradigms aim to maximize the cumulative reward when interacting with the unknown environment., i. e., maximize $\sum_{h = 1}^H r_h$ where $H$ is the planning horizon.
Mixtape: Breaking the Softmax Bottleneck Efficiently
The softmax bottleneck has been shown to limit the expressiveness of neural language models.
Multiple Futures Prediction
Towards these goals, we introduce a probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene.
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