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Automatic Data Augmentation for Generalization in Deep RL
We compare three approaches for automatically finding an appropriate augmentation combined with two novel regularization terms for the policy and value ...
data-augmentation reinforcement-learning kornia pytorch
Plan2Explore: Plan to Explore via Self-Supervised World Models
A self-supervised reinforcement learning agent that tackles task-specific and the sample efficiency challenges.
self-supervised-learning reinforcement-learning plan2explore article
Deep Reinforcement Learning Amidst Lifelong Non-Stationarity
How can robots learn in changing, open-world environments? We introduce dynamic-parameter MDPs, to capture environments with persistent, unobserved ...
reinforcement-learning non-stationarity off-policy markov-decision-process
Model-based Reinforcement Learning: A Survey
A survey of the integration of both fields, better known as model-based reinforcement learning.
reinforcement-learning markov-decision-process model-based-reinforcement-learning survey
Using Selective Attention in Reinforcement Learning Agents
In this work, we establish that self-attention can be viewed as a form of indirect encoding, which enables us to construct highly parameter-efficient ...
reinforcement-learning self-attention selective-attention neuroevolution
Model-Agnostic Meta-Learning for Reinforcement Learning with TF2
Reimplementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in TensorFlow 2.
meta-learning reinforcement-learning tensorflow code
Short Notes on Model-Based Offline Reinforcement Learning (MOReL)
Blog article on Model-Based Offline Reinforcement Learning (MOReL) paper by Rahul Kidambi & Aravind Rajeswaran et al.
reinforcement-learning model-based offline-rl code
Short Notes on Behavior Regularized Offline RL
Blog Article on Behavior Regularized Offline Reinforcement Learning by Yifan Wu et al. (2019)
reinforcement-learning q-learning actor-critic tutorial
Short Notes on Batch Constrained Deep Reinforcement Learning
Blog article on Off-Policy Deep Reinforcement Learning without Exploration paper by Fujimoto et al. (ICML 2019)
reinforcement-learning q-learning batch-rl tutorial
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