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 4801–4825 of 15113 papers

TitleStatusHype
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning—0
On Efficient Reinforcement Learning for Full-length Game of StarCraft IICode2
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning—0
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement LearningCode0
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation—0
Reinforcement Learning in Computing and Network Convergence Orchestration—0
Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning—0
Identifiability and generalizability from multiple experts in Inverse Reinforcement LearningCode0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
An Investigation of the Bias-Variance Tradeoff in Meta-GradientsCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions—0
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving—0
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning—0
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games—0
Hierarchical Decision Transformer—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
Revisiting Discrete Soft Actor-CriticCode1
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies—0
Model-Free Reinforcement Learning for Asset Allocation—0
Towards Task-Prioritized Policy Composition—0
Optimizing Crop Management with Reinforcement Learning and Imitation Learning—0
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Benchmark Results

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