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 4651–4660 of 15113 papers

TitleStatusHype
B-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis—0
Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationCode0
Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
Blending Imitation and Reinforcement Learning for Robust Policy Improvement—0
A Deep Reinforcement Learning Approach for Interactive Search with Sentence-level Feedback—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
On Representation Complexity of Model-based and Model-free Reinforcement Learning—0
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement LearningCode0
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Benchmark Results

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