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 4226–4250 of 15113 papers

TitleStatusHype
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement LearningCode0
Tractable Reinforcement Learning of Signal Temporal Logic ObjectivesCode0
On Improving Deep Reinforcement Learning for POMDPsCode0
ROER: Regularized Optimal Experience ReplayCode0
Rogue-Gym: A New Challenge for Generalization in Reinforcement LearningCode0
ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender SystemsCode0
Training Adversarial Agents to Exploit Weaknesses in Deep Control PoliciesCode0
Training Agents using Upside-Down Reinforcement LearningCode0
Mode-constrained Model-based Reinforcement Learning via Gaussian ProcessesCode0
Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement LearningCode0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
ROS2Learn: a reinforcement learning framework for ROS 2Code0
Rotation, Translation, and Cropping for Zero-Shot GeneralizationCode0
Training Transition Policies via Distribution Matching for Complex TasksCode0
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning AgentsCode0
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement LearningCode0
Trajectory-Based Off-Policy Deep Reinforcement LearningCode0
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement LearningCode0
Regularizing Neural Networks by Penalizing Confident Output DistributionsCode0
RUDDER: Return Decomposition for Delayed RewardsCode0
Rule Augmented Unsupervised Constituency ParsingCode0
Regularizing Neural Networks for Future Trajectory Prediction via Inverse Reinforcement Learning FrameworkCode0
On Instrumental Variable Regression for Deep Offline Policy EvaluationCode0
Run, skeleton, run: skeletal model in a physics-based simulationCode0
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement LearningCode0
Show:102550
← PrevPage 170 of 605Next →

Benchmark Results

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