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 10551–10575 of 15113 papers

TitleStatusHype
Evaluating Pretrained models for Deployable Lifelong Learning—0
Evaluating Reinforcement Learning Algorithms in Observational Health Settings—0
Evaluating Reinforcement Learning Safety and Trustworthiness in Cyber-Physical Systems—0
Evaluating Robustness of Cooperative MARL—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping—0
Evaluating State Representations for Reinforcement Learning of Turn-Taking Policies in Tutorial Dialogue—0
Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach—0
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning—0
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification—0
Evaluating Vision Transformer Methods for Deep Reinforcement Learning from Pixels—0
Evaluation of Active Feature Acquisition Methods for Static Feature Settings—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning—0
Evaluation of Online Dialogue Policy Learning Techniques—0
Evaluation-Time Policy Switching for Offline Reinforcement Learning—0
Event Discovery for History Representation in Reinforcement Learning—0
Event-Driven Models—0
Event Extraction with Generative Adversarial Imitation Learning—0
Event Identification as a Decision Process with Non-linear Representation of Text—0
Event Tables for Efficient Experience Replay—0
Evolution and The Knightian Blindspot of Machine Learning—0
Evolutionarily-Curated Curriculum Learning for Deep Reinforcement Learning Agents—0
Evolutionary algorithms for constructing an ensemble of decision trees—0
Evolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks—0
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Benchmark Results

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