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 4851–4875 of 15113 papers

TitleStatusHype
An information-theoretic perspective on intrinsic motivation in reinforcement learning: a survey—0
Evolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks—0
Multi-level Explanation of Deep Reinforcement Learning-based Scheduling—0
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes—0
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective—0
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Intrinsically Motivated Reinforcement Learning based Recommendation with Counterfactual Data Augmentation—0
A Robust and Constrained Multi-Agent Reinforcement Learning Electric Vehicle Rebalancing Method in AMoD Systems—0
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning—0
Selective Token Generation for Few-shot Natural Language GenerationCode0
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control—0
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control—0
Toward Safe and Accelerated Deep Reinforcement Learning for Next-Generation Wireless NetworksCode1
Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning—0
Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning—0
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability—0
Model Inversion Attacks against Graph Neural Networks—0
Model-based gym environments for limit order book tradingCode1
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement LearningCode1
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage ControlCode1
Reinforcement Learning-Based Cooperative P2P Power Trading between DC Nanogrid Clusters with Wind and PV Energy Resources—0
Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES—0
M^2DQN: A Robust Method for Accelerating Deep Q-learning NetworkCode0
Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning—0
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement LearningCode0
Show:102550
← PrevPage 195 of 605Next →

Benchmark Results

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