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Found 6 papers, 0 papers with code
Adapting World Models with Latent-State Dynamics Residuals
Simulation-to-reality reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade agent performance.
Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Training in the presence of visual distractions is particularly difficult due to the high variation they introduce to representation learning.
Realizable Continuous-Space Shields for Safe Reinforcement Learning
For real-world robotic domains, it is essential to define safety specifications over continuous state and action spaces to accurately account for system dynamics and compute new actions that minimally deviate from the agent's original decision.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities.
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments
In this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem formulation and objective for automatically determining the set of environment parameter values over which to be robust.
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games
Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well.
