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Found 13 papers, 12 papers with code
In-Context Reinforcement Learning for Variable Action Spaces
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research.
Emergence of In-Context Reinforcement Learning from Noise Distillation
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks.
Revisiting the Minimalist Approach to Offline Reinforcement Learning
Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
Anti-Exploration by Random Network Distillation
Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline reinforcement learning.
Let Offline RL Flow: Training Conservative Agents in the Latent Space of Normalizing Flows
This Normalizing Flows action encoder is pre-trained in a supervised manner on the offline dataset, and then an additional policy model - controller in the latent space - is trained via reinforcement learning.
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Training large neural networks is known to be time-consuming, with the learning duration taking days or even weeks.
CORL: Research-oriented Deep Offline Reinforcement Learning Library
CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters
In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms.
Guiding Evolutionary Strategies by Differentiable Robot Simulators
In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms.
Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search
In this paper, we consider tensegrity hopper - a novel tensegrity-based robot, capable of moving by hopping.
Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order
In this work, we analyze the performance of general deep reinforcement learning algorithms for a task-oriented language grounding problem, where language input contains multiple sub-goals and their order of execution is non-linear.
