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 90019050 of 15113 papers

TitleStatusHype
Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning0
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels0
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments0
Reinforcement Learning for Flexibility Design Problems0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning0
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection ApproachCode0
Deep Reinforcement Learning-based Anti-jamming Power Allocation in a Two-cell NOMA Network0
A Survey on Deep Reinforcement Learning for Audio-Based Applications0
Inverse reinforcement learning for autonomous navigation via differentiable semantic mapping and planning0
When Is Generalizable Reinforcement Learning Tractable?0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning0
Multi-Agent Trust Region LearningCode1
Batch Reinforcement Learning Through Continuation Method0
Hierarchical Meta Reinforcement Learning for Multi-Task EnvironmentsCode0
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
Learning to Observe with Reinforcement Learning0
A Simple Sparse Denoising Layer for Robust Deep Learning0
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING0
Intrinsically Guided Exploration in Meta Reinforcement Learning0
A Reduction Approach to Constrained Reinforcement Learning0
Invariant Representations for Reinforcement Learning without Reconstruction0
Guiding Representation Learning in Deep Generative Models with Policy Gradients0
FactoredRL: Leveraging Factored Graphs for Deep Reinforcement Learning0
Exploring Transferability of Perturbations in Deep Reinforcement Learning0
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents0
Incremental Policy Gradients for Online Reinforcement Learning Control0
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation0
CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature0
Explainable Reinforcement Learning Through Goal-Based Explanations0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning0
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs0
Constrained Reinforcement Learning With Learned Constraints0
Error Controlled Actor-Critic Method to Reinforcement Learning0
Learning to Explore with Pleasure0
Learning Efficient Planning-based Rewards for Imitation Learning0
Average Reward Reinforcement Learning with Monotonic Policy Improvement0
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning0
Deep Reinforcement Learning With Adaptive Combined Critics0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation0
Discrete Predictive Representation for Long-horizon Planning0
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay0
Learning Predictive Communication by Imagination in Networked System Control0
Learning Safe Policies with Cost-sensitive Advantage Estimation0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning0
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning0
Self-Supervised Continuous Control without Policy Gradient0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning0
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous ControlCode0
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Benchmark Results

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