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 9401–9425 of 15113 papers

TitleStatusHype
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods—0
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Reinforcement learning approach for resource allocation in humanitarian logistics—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians—0
On The Effect of Auxiliary Tasks on Representation Dynamics—0
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation—0
The Logical Options Framework—0
Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal LogicCode0
Towards Safe Continuing Task Reinforcement Learning—0
PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference LearningCode0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Fast Approximate Solutions using Reinforcement Learning for Dynamic Capacitated Vehicle Routing with Time Windows—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning—0
Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Combining Off and On-Policy Training in Model-Based Reinforcement Learning—0
Greedy-Step Off-Policy Reinforcement Learning—0
Differentiable Logic Machines—0
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
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Benchmark Results

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