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Reinforcement Learning
InfoBot: Transfer and exploration via the information bottleneck (Apr 2019)
Using information bottleneck (variational encoder) in agent to improve generalization and exploration of states.
Exploration by Random Network Distillation [code] (Oct 2018)
RND incentivizes visiting unfamiliar states by measuring how hard it is to predict the output of a fixed random neural network on visited states.
Self-Imitation Learning (Jun 2018)
Self-imitation learning improves actor-critic (A2C) agents to explore deeper.
Imagination-Augmented Agents for Deep Reinforcement Learning (Feb 2018)
Imagination-Augmented Agents (I2As) learns to interpret predictions from a learned environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks.
Proximal Policy Optimization Algorithms [code] (Jul 2017)
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