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Found 4 papers, 2 papers with code
Maintaining Plasticity in Deep Continual Learning
If deep-learning systems are applied in a continual learning setting, then it is well known that they may fail to remember earlier examples.
Learning Sparse Representations Incrementally in Deep Reinforcement Learning
Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumulative reward.
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target
Our results show that (1) using off-policy correction can have an adverse effect on the performance of Sarsa and $Q(\sigma)$; (2) increasing the backup length $n$ consistently improved performance across all the different algorithms; and (3) the performance of Sarsa and $Q$-learning was more robust to the effect of the target network update frequency than the performance of Tree Backup, $Q(\sigma)$, and Retrace in this particular task.
Multi-step Reinforcement Learning: A Unifying Algorithm
These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance.
