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Found 13 papers, 8 papers with code
Synthetic Experience Replay
We believe that synthetic training data could open the door to realizing the full potential of deep learning for replay-based RL algorithms from limited data.
Efficient Online Reinforcement Learning with Offline Data
Sample efficiency and exploration remain major challenges in online reinforcement learning (RL).
Learning General World Models in a Handful of Reward-Free Deployments
We then present CASCADE, a novel approach for self-supervised exploration in this new setting.
Bayesian Generational Population-Based Training
Leveraging the new highly parallelizable Brax physics engine, we show that these innovations lead to large performance gains, significantly outperforming the tuned baseline while learning entire configurations on the fly.
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable.
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
Using this suite of benchmarking tasks, we show that simple modifications to two popular vision-based online reinforcement learning algorithms, DreamerV2 and DrQ-v2, suffice to outperform existing offline RL methods and establish competitive baselines for continuous control in the visual domain.
Revisiting Design Choices in Offline Model-Based Reinforcement Learning
Significant progress has been made recently in offline model-based reinforcement learning, approaches which leverage a learned dynamics model.
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment
Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration.
OffCon$^3$: What is state of the art anyway?
Two popular approaches to model-free continuous control tasks are SAC and TD3.
A Study on Efficiency in Continual Learning Inspired by Human Learning
We study a setting where the pruning phase is given a time budget, and identify connections between iterative pruning and multiple sleep cycles in humans.
Towards Tractable Optimism in Model-Based Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL).
Active inference: demystified and compared
In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.
