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 3726–3750 of 15113 papers

TitleStatusHype
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing—0
Continuous Control with Coarse-to-fine Reinforcement Learning—0
Token-Mol 1.0: Tokenized drug design with large language model—0
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning—0
Structural Design Through Reinforcement LearningCode0
Preference-Guided Reinforcement Learning for Efficient ExplorationCode0
Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Periodic agent-state based Q-learning for POMDPs—0
On Bellman equations for continuous-time policy evaluation I: discretization and approximation—0
An open source Multi-Agent Deep Reinforcement Learning Routing Simulator for satellite networks—0
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments—0
FOSP: Fine-tuning Offline Safe Policy through World Models—0
Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based ShieldingCode0
Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents—0
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling—0
Using Petri Nets as an Integrated Constraint Mechanism for Reinforcement Learning Tasks—0
ROER: Regularized Optimal Experience ReplayCode0
Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language ModelsCode0
Warm-up Free Policy Optimization: Improved Regret in Linear Markov Decision Processes—0
PWM: Policy Learning with Multi-Task World Models—0
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement LearningCode0
Reinforcement Learning-driven Data-intensive Workflow Scheduling for Volunteer Edge-Cloud—0
To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning—0
Safe Reinforcement Learning for Power System Control: A Review—0
Show:102550
← PrevPage 150 of 605Next →

Benchmark Results

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