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Found 8 papers, 3 papers with code
Expert Proximity as Surrogate Rewards for Single Demonstration Imitation Learning
In this paper, we focus on single-demonstration imitation learning (IL), a practical approach for real-world applications where obtaining numerous expert demonstrations is costly or infeasible.
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories
With extensive experimental evaluation, we show the prevalence of \emph{generalization failure} on controllable states from stranger agents.
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions.
Learning to Schedule Learning rate with Graph Neural Networks
By constructing a directed graph for the underlying neural network of the target problem, GNS encodes current dynamics with a graph message passing network and trains an agent to control the learning rate accordingly via reinforcement learning.
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search
This implies that a significant amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result.
How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers
For end-to-end efficiency, unlike previous work that assumes random hyperparameter tuning, which over-emphasizes the tuning time, we propose to evaluate with a bandit hyperparameter tuning strategy.
Multiple Policy Value Monte Carlo Tree Search
Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluators.
Multi-Labelled Value Networks for Computer Go
First, the MSE of the ML value network is generally lower than the value network alone.
