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 14311440 of 15113 papers

TitleStatusHype
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Compile Scene Graphs with Reinforcement LearningCode1
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingCode1
Hindsight Experience ReplayCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed SystemsCode1
How Can LLM Guide RL? A Value-Based ApproachCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
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Benchmark Results

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