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

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Showing 3661–3670 of 15113 papers

TitleStatusHype
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Preference Transformer: Modeling Human Preferences using Transformers for RLCode1
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Benchmark Results

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