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 3276–3300 of 15113 papers

TitleStatusHype
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable PoliciesCode0
Modular Multi-Objective Deep Reinforcement Learning with Decision ValuesCode0
Modular Multitask Reinforcement Learning with Policy SketchesCode0
Hindsight policy gradientsCode0
ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill DiscoveryCode0
A Threshold-based Scheme for Reinforcement Learning in Neural NetworksCode0
Dealing with Sparse Rewards in Reinforcement LearningCode0
Computing the Feedback Capacity of Finite State Channels using Reinforcement LearningCode0
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill DiscoveryCode0
Reinforcement Learning from Hierarchical CriticsCode0
DEAR: Disentangled Environment and Agent Representations for Reinforcement Learning without ReconstructionCode0
Hierarchical Decentralized Deep Reinforcement Learning Architecture for a Simulated Four-Legged AgentCode0
Heuristics, Answer Set Programming and Markov Decision Process for Solving a Set of Spatial PuzzlesCode0
A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control SystemsCode0
Active One-shot LearningCode0
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic MotivationCode0
HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI GymCode0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
A reinforcement learning approach to rare trajectory samplingCode0
Health-Informed Policy Gradients for Multi-Agent Reinforcement LearningCode0
Harnessing Structures for Value-Based Planning and Reinforcement LearningCode0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RLCode0
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned MessagingCode0
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Benchmark Results

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