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Found 5 papers, 4 papers with code
Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
In this work, we introduce Lucid Dreaming for Experience Replay (LiDER), a conceptually new framework that allows replay experiences to be refreshed by leveraging the agent's current policy.
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient.
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images.
Adapting Auxiliary Losses Using Gradient Similarity
One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary losses that help to build useful representations.
Pre-training Neural Networks with Human Demonstrations for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using a deep neural network as its function approximator and by learning directly from raw images.
