Efficient Exploration
144 papers with code • 0 benchmarks • 2 datasets
Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.
Source: Randomized Value Functions via Multiplicative Normalizing Flows
Benchmarks
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Libraries
Use these libraries to find Efficient Exploration models and implementations
Replica
House3D Environment
Latest papers
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation
18979705623/hspr
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Most Vision-and-Language Navigation (VLN) algorithms tend to make decision errors, primarily due to a lack of visual common sense and insufficient reasoning capabilities.
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Adversarial Multi-agent Games
Reinforcement Learning- (RL-)based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation.
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
zoharri/mamba
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Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
Scalable Online Exploration via Coverability
We propose exploration objectives -- policy optimization objectives that enable downstream maximization of any reward function -- as a conceptual framework to systematize the study of exploration.
Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models (LMs) and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios.
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
jarridrb/dem
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Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
Safe Guaranteed Exploration for Non-linear Systems
Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control.
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space.
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
maho3/ltu-ili
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This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis
Our method treats strategy synthesis as a sequential decision-making process, whose search tree corresponds to the strategy space, and employs MCTS to navigate this vast search space.
