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 3176–3200 of 15113 papers

TitleStatusHype
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback—0
A Survey on Reinforcement Learning for Recommender Systems—0
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles—0
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Cycle Consistency Driven Object Discovery—0
A Survey on Reinforcement Learning Methods in Character Animation—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards—0
D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning—0
A Survey on Traffic Signal Control Methods—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Agent Probing Interaction Policies—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
DanZero: Mastering GuanDan Game with Reinforcement Learning—0
DAQN: Deep Auto-encoder and Q-Network—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
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Benchmark Results

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