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 8726–8750 of 15113 papers

TitleStatusHype
Improved Learning of Robot Manipulation Tasks via Tactile Intrinsic Motivation—0
Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization—0
Communication Efficient Parallel Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR—0
Learning Efficient Navigation in Vortical Flow Fields—0
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Safe Reinforcement Learning Using Robust Action Governor—0
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach—0
How To Train Your HERON—0
Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment—0
Decaying Clipping Range in Proximal Policy OptimizationCode0
Decoupling Value and Policy for Generalization in Reinforcement LearningCode1
Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent SpaceCode1
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning—0
A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ—0
Decentralized Deterministic Multi-Agent Reinforcement Learning—0
Sim-Env: Decoupling OpenAI Gym Environments from Simulation ModelsCode0
TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning—0
Model-Invariant State Abstractions for Model-Based Reinforcement Learning—0
Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems—0
Adaptive Rational Activations to Boost Deep Reinforcement LearningCode1
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics ScenesCode1
Smart Feasibility Pump: Reinforcement Learning for (Mixed) Integer Programming—0
State Entropy Maximization with Random Encoders for Efficient ExplorationCode1
SeaPearl: A Constraint Programming Solver guided by Reinforcement LearningCode1
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Benchmark Results

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