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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7026–7050 of 15113 papers

TitleStatusHype
Environment Shaping in Reinforcement Learning using State Abstraction—0
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines—0
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning—0
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Episodic Memory Deep Q-Networks—0
Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task—0
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited—0
Episodic Reinforcement Learning with Associative Memory—0
Epistemic Risk-Sensitive Reinforcement Learning—0
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning—0
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Equivalence Between Policy Gradients and Soft Q-Learning—0
Equivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning—0
Equivalence of Optimality Criteria for Markov Decision Process and Model Predictive Control—0
Equivariant Action Sampling for Reinforcement Learning and Planning—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
Equivariant MuZero—0
Equivariant Offline Reinforcement Learning—0
Equivariant Reinforcement Learning for Quadrotor UAV—0
Ergodic Annealing—0
Error Bounds of Imitating Policies and Environments—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
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Benchmark Results

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