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 5176–5200 of 15113 papers

TitleStatusHype
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning—0
Remote Electrical Tilt Optimization via Safe Reinforcement Learning—0
Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients—0
Rendering-Aware Reinforcement Learning for Vector Graphics Generation—0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Renewal Monte Carlo: Renewal theory based reinforcement learning—0
Rényi State Entropy for Exploration Acceleration in Reinforcement Learning—0
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
Reparameterized Policy Learning for Multimodal Trajectory Optimization—0
Repeated Inverse Reinforcement Learning—0
Replay across Experiments: A Natural Extension of Off-Policy RL—0
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning—0
Replicability in Reinforcement Learning—0
Replicating Complex Dialogue Policy of Humans via Offline Imitation Learning with Supervised Regularization—0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Representational efficiency outweighs action efficiency in human program induction—0
Representation and Invariance in Reinforcement Learning—0
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients—0
Representation Balancing Offline Model-based Reinforcement Learning—0
Representation-based Reward Modeling for Efficient Safety Alignment of Large Language Model—0
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning—0
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning—0
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Benchmark Results

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