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

Showing 26262650 of 15113 papers

TitleStatusHype
AI Safety GridworldsCode0
Is Deep Reinforcement Learning Really Superhuman on Atari? Leveling the playing fieldCode0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement LearningCode0
Inverse reinforcement learning for video gamesCode0
Inverse Reinforcement Learning in Contextual MDPsCode0
Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement LearningCode0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement LearningCode0
Intrinsic fluctuations of reinforcement learning promote cooperationCode0
Iterative Reward Shaping using Human Feedback for Correcting Reward MisspecificationCode0
Hierarchical Reinforcement Learning with AI Planning ModelsCode0
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Attentive Multi-Task Deep Reinforcement LearningCode0
Interestingness Elements for Explainable Reinforcement Learning: Understanding Agents' Capabilities and LimitationsCode0
Interactive Query-Assisted Summarization via Deep Reinforcement LearningCode0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement LearningCode0
Interactive Learning from Activity DescriptionCode0
On the Correctness and Sample Complexity of Inverse Reinforcement LearningCode0
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement LearningCode0
Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing TasksCode0
Intelligent Traffic Light via Policy-based Deep Reinforcement LearningCode0
A Centralised Soft Actor Critic Deep Reinforcement Learning Approach to District Demand Side Management through CityLearnCode0
Attention-based Curiosity-driven Exploration in Deep Reinforcement LearningCode0
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Benchmark Results

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