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 1160111650 of 15113 papers

TitleStatusHype
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings0
RELDEC: Reinforcement Learning-Based Decoding of Moderate Length LDPC Codes0
ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources0
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks0
Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning0
Reliable Off-policy Evaluation for Reinforcement Learning0
Reliable validation of Reinforcement Learning Benchmarks0
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation0
ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation0
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression0
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning0
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning0
Remote Electrical Tilt Optimization via Safe Reinforcement Learning0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients0
Rendering-Aware Reinforcement Learning for Vector Graphics Generation0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning0
Renewal Monte Carlo: Renewal theory based reinforcement learning0
Rényi State Entropy for Exploration Acceleration in Reinforcement Learning0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning0
Reparameterized Policy Learning for Multimodal Trajectory Optimization0
Repeated Inverse Reinforcement Learning0
Replay across Experiments: A Natural Extension of Off-Policy RL0
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning0
Replicability in Reinforcement Learning0
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning0
Representational efficiency outweighs action efficiency in human program induction0
Representation and Invariance in Reinforcement Learning0
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients0
Representation Balancing Offline Model-based Reinforcement Learning0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model0
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning0
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning0
The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents0
Representation Learning for Out-of-distribution Generalization in Reinforcement Learning0
Representation Learning in Deep RL via Discrete Information Bottleneck0
Representation Learning in Low-rank Slate-based Recommender Systems0
Representation Learning on Graphs: A Reinforcement Learning Application0
Representation Matters: Offline Pretraining for Sequential Decision Making0
Representations for Stable Off-Policy Reinforcement Learning0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients0
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents0
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework0
RESEARCH ARTICLE A Reinforcement Learning Model of Joy, Distress, Hope and Fear0
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Benchmark Results

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