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 8676–8700 of 15113 papers

TitleStatusHype
Model-free Reinforcement Learning for Robust Locomotion using Demonstrations from Trajectory Optimization—0
Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning—0
Centralized Model and Exploration Policy for Multi-Agent RLCode0
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments—0
Deep Adaptive Multi-Intention Inverse Reinforcement LearningCode0
Going Beyond Linear RL: Sample Efficient Neural Function Approximation—0
A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization—0
Cautious Policy Programming: Exploiting KL Regularization in Monotonic Policy Improvement for Reinforcement Learning—0
Carle's Game: An Open-Ended Challenge in Exploratory Machine CreativityCode0
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage—0
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability—0
Model Selection for Generic Reinforcement Learning—0
Modeling Explicit Concerning States for Reinforcement Learning in Visual DialogueCode0
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents—0
R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery—0
Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire—0
Polynomial Time Reinforcement Learning in Factored State MDPs with Linear Value Functions—0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
CoBERL: Contrastive BERT for Reinforcement Learning—0
Behavior Constraining in Weight Space for Offline Reinforcement Learning—0
Generating stable molecules using imitation and reinforcement learning—0
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots—0
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative TasksCode0
NVCell: Standard Cell Layout in Advanced Technology Nodes with Reinforcement Learning—0
Safe Exploration by Solving Early Terminated MDP—0
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Benchmark Results

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