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 8276–8300 of 15113 papers

TitleStatusHype
Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning—0
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization—0
Self-Supervised Structured Representations for Deep Reinforcement Learning—0
Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management—0
Theoretical understanding of adversarial reinforcement learning via mean-field optimal control—0
Multi-batch Reinforcement Learning via Sample Transfer and Imitation Learning—0
The Remarkable Effectiveness of Combining Policy and Value Networks in A*-based Deep RL for AI Planning—0
Offline-Online Reinforcement Learning: Extending Batch and Online RL—0
P4O: Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization—0
Rethinking Pareto Approaches in Constrained Reinforcement Learning—0
Offline Pre-trained Multi-Agent Decision Transformer—0
Should I Run Offline Reinforcement Learning or Behavioral Cloning?—0
Selective Token Generation for Few-shot Language Modeling—0
Offline Reinforcement Learning for Large Scale Language Action Spaces—0
Task-driven Discovery of Perceptual Schemas for Generalization in Reinforcement Learning—0
Targeted Environment Design from Offline Data—0
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Offline Reinforcement Learning with Resource Constrained Online Deployment—0
Towards Understanding Distributional Reinforcement Learning: Regularization, Optimization, Acceleration and Sinkhorn Algorithm—0
Towards Unknown-aware Deep Q-Learning—0
Model-based Reinforcement Learning with Ensembled Model-value Expansion—0
Rewardless Open-Ended Learning (ROEL)—0
Transformers are Meta-Reinforcement Learners—0
Triangular Dropout: Variable Network Width without Retraining—0
MOBA: Multi-teacher Model Based Reinforcement Learning—0
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Benchmark Results

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