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Found 12 papers, 8 papers with code
Policy-Guided Diffusion
Our approach provides an effective alternative to autoregressive offline world models, opening the door to the controllable generation of synthetic training data.
The Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
The prevailing theoretical understanding is that this can then be viewed as online reinforcement learning in an approximate dynamics model, and any remaining gap is therefore assumed to be due to the imperfect dynamics model.
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.
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations
KL-regularized reinforcement learning from expert demonstrations has proved successful in improving the sample efficiency of deep reinforcement learning algorithms, allowing them to be applied to challenging physical real-world tasks.
Go-Explore Complex 3D Game Environments for Automated Reachability Testing
Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively.
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.
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.
Revisiting Design Choices in Offline Model Based Reinforcement Learning
Offline reinforcement learning enables agents to make use of large pre-collected datasets of environment transitions and learn control policies without the need for potentially expensive or unsafe online data collection.
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.
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
High-dimensional black-box optimisation remains an important yet notoriously challenging problem.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning
To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep.
