Reinforcement Learning
Meta-Learning Representations for Continual Learning
Moreover, our algorithm is complementary to existing continual learning strategies, such as MER and GEM.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, Sergey Levine
Neural Information Processing Systems http://nips.cc/
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Hajin Shim, Sung Ju Hwang, Eunho Yang
Neural Information Processing Systems http://nips.cc/
Can Pre-Trained Text-to-Image Models Generate Visual Goals for Reinforcement Learning? Jialu Gao
Subsequently, LfV oid trains an ensembled goal discriminator on the generated image to provide reward signals for a reinforcement learning agent, guiding it to achieve the goal.
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models Lucas N. Alegre 1, 2 Ana L. C. Bazzan 1 Ann Now é 2 Bruno C. da Silva 3 1
We introduce a principled method for performing zero-shot transfer in reinforcement learning (RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has been investigated by leveraging methods rooted in generalized policy improvement (GPI) and successor features (SFs).