SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3276–3300 of 15113 papers

TitleStatusHype
Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of BeamlinesCode0
Regret-Free Reinforcement Learning for LTL Specifications—0
No-regret Exploration in Shuffle Private Reinforcement Learning—0
Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation—0
Robust Reinforcement Learning under Diffusion Models for Data with Jumps—0
Continual Task Learning through Adaptive Policy Self-CompositionCode0
Upside-Down Reinforcement Learning for More Interpretable Optimal Control—0
Preserving Expert-Level Privacy in Offline Reinforcement Learning—0
Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents—0
Modulating Reservoir Dynamics via Reinforcement Learning for Efficient Robot Skill Synthesis—0
An Investigation of Offline Reinforcement Learning in Factorisable Action SpacesCode0
Financial News-Driven LLM Reinforcement Learning for Portfolio Management—0
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay—0
Adaptive Learning of Design Strategies over Non-Hierarchical Multi-Fidelity Models via Policy Alignment—0
Multi-agent Path Finding for Timed Tasks using Evolutionary Games—0
Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review—0
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability—0
The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning—0
To bootstrap or to rollout? An optimal and adaptive interpolation—0
Innate-Values-driven Reinforcement Learning based Cognitive Modeling—0
Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literatureCode0
Towards Practical Deep Schedulers for Allocating Cellular Radio Resources—0
Robot See, Robot Do: Imitation Reward for Noisy Financial Environments—0
RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm—0
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified