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

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Showing 1126–1150 of 15113 papers

TitleStatusHype
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement LearningCode1
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement LearningCode1
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and ClassificationCode1
Efficient Risk-Averse Reinforcement LearningCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning ApproachCode1
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
An Introduction to Deep Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Efficient Diffusion Policies for Offline Reinforcement LearningCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Efficient Reinforcement Learning Through Trajectory GenerationCode1
BabyAI 1.1Code1
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
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Benchmark Results

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