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 4976–5000 of 15113 papers

TitleStatusHype
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning—0
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control—0
Replicability in Reinforcement Learning—0
MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RL—0
Robust Reinforcement Learning Objectives for Sequential Recommender SystemsCode0
Policy Optimization for Continuous Reinforcement Learning—0
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion—0
Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple ReuseCode0
Towards a Better Understanding of Representation Dynamics under TD-learning—0
Bridging the Sim-to-Real Gap from the Information Bottleneck PerspectiveCode0
RLAD: Reinforcement Learning from Pixels for Autonomous Driving in Urban Environments—0
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles—0
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model—0
Reinforcement Learning with Simple Sequence Priors—0
Policy Synthesis and Reinforcement Learning for Discounted LTL—0
Emergent Agentic Transformer from Chain of Hindsight Experience—0
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
End-to-End Meta-Bayesian Optimisation with Transformer Neural ProcessesCode0
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion ModelsCode0
Deterministic policy gradient based optimal control with probabilistic constraints—0
Reward-Machine-Guided, Self-Paced Reinforcement LearningCode0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
Decision-Aware Actor-Critic with Function Approximation and Theoretical GuaranteesCode0
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Benchmark Results

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