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 6176–6200 of 15113 papers

TitleStatusHype
Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement LearningCode0
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation—0
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments—0
An Investigation of the Bias-Variance Tradeoff in Meta-GradientsCode0
Identifiability and generalizability from multiple experts in Inverse Reinforcement LearningCode0
Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning—0
Learning from Symmetry: Meta-Reinforcement Learning with Symmetrical Behaviors and Language Instructions—0
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games—0
ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving—0
Hierarchical Decision Transformer—0
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning—0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Model-Free Reinforcement Learning for Asset Allocation—0
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies—0
Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks—0
Towards Task-Prioritized Policy Composition—0
Optimizing Crop Management with Reinforcement Learning and Imitation Learning—0
Soft Action Priors: Towards Robust Policy Transfer—0
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability—0
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline RegretCode0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning—0
Deep Q-Network for AI Soccer—0
A Spiking Neural Network Learning Markov Chain—0
Locally Constrained Representations in Reinforcement Learning—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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