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Found 16 papers, 6 papers with code
Replay-enhanced Continual Reinforcement Learning
On the other hand, offline learning on replayed tasks while learning a new task may induce a distributional shift between the dataset and the learned policy on old tasks, resulting in forgetting.
Future-conditioned Unsupervised Pretraining for Decision Transformer
While promising, return conditioning is limited to training data labeled with rewards and therefore faces challenges in learning from unsupervised data.
Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization
Recent success in Deep Reinforcement Learning (DRL) methods has shown that policy optimization with respect to an off-policy distribution via importance sampling is effective for sample reuse.
A Survey on Transformers in Reinforcement Learning
Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings.
Pretraining in Deep Reinforcement Learning: A Survey
The past few years have seen rapid progress in combining reinforcement learning (RL) with deep learning.
Revisiting Discrete Soft Actor-Critic
We study the adaption of soft actor-critic (SAC) from continuous action space to discrete action space.
Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information.
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned
With this in mind, we hosted the third edition of the MineRL ObtainDiamond competition, MineRL Diamond 2021, with a separate track in which we permitted any solution to promote the participation of newcomers.
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning
To address this, we propose JueWu-MC, a sample-efficient hierarchical RL approach equipped with representation learning and imitation learning to deal with perception and exploration.
Efficient Exploration
Hierarchical Reinforcement Learning
+4
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System
Recent work (Takanobu et al., 2020) proposed the system-wise evaluation on dialog systems and found that improvement on individual components (e. g., NLU, policy) in prior work may not necessarily bring benefit to pipeline systems in system-wise evaluation.
RD$^2$: Reward Decomposition with Representation Decomposition
In this work, we propose a set of novel reward decomposition principles by constraining uniqueness and compactness of different state features/representations relevant to different sub-rewards.
Model-based Adversarial Meta-Reinforcement Learning
When the test task distribution is different from the training task distribution, the performance may degrade significantly.
Distributional Reward Decomposition for Reinforcement Learning
Many reinforcement learning (RL) tasks have specific properties that can be leveraged to modify existing RL algorithms to adapt to those tasks and further improve performance, and a general class of such properties is the multiple reward channel.
Fully Parameterized Quantile Function for Distributional Reinforcement Learning
The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
Object-Oriented Dynamics Learning through Multi-Level Abstraction
We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability.
Episodic Memory Deep Q-Networks
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN).
