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 1076–1100 of 15113 papers

TitleStatusHype
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Efficient Risk-Averse Reinforcement LearningCode1
Eigenoption Discovery through the Deep Successor RepresentationCode1
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based NavigationCode1
A Distributional Perspective on Reinforcement LearningCode1
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy GamesCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Efficient Meta Reinforcement Learning for Preference-based Fast AdaptationCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Effective and Transparent RAG: Adaptive-Reward Reinforcement Learning for Decision TraceabilityCode1
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Benchmark Results

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