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 1311–1320 of 15113 papers

TitleStatusHype
Sequential Voting with Relational Box Fields for Active Object DetectionCode1
Hierarchical Skills for Efficient ExplorationCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Offline Reinforcement Learning with Value-based Episodic MemoryCode1
Contrastive Active InferenceCode1
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentsCode1
No RL, No Simulation: Learning to Navigate without NavigatingCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
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Benchmark Results

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