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 10776–10800 of 15113 papers

TitleStatusHype
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning—0
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation—0
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control—0
Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning—0
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning—0
The Overthinker's DIET: Cutting Token Calories with DIfficulty-AwarE Training—0
The Partially Observable History Process—0
The Phenomenon of Policy Churn—0
The Political Preferences of LLMs—0
The Power of Communication in a Distributed Multi-Agent System—0
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios—0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Reality—0
The Recurrent Reinforcement Learning Crypto Agent—0
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes—0
There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning—0
Reward Maximisation through Discrete Active Inference—0
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning—0
The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models—0
The RL Perceptron: Generalisation Dynamics of Policy Learning in High Dimensions—0
A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments—0
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning—0
The Role of Coverage in Online Reinforcement Learning—0
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Benchmark Results

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