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 8901–8925 of 15113 papers

TitleStatusHype
Placement Optimization with Deep Reinforcement Learning—0
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks—0
Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning—0
Plan-Based Asymptotically Equivalent Reward Shaping—0
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Planning and Learning with Stochastic Action Sets—0
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space—0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Planning with Abstract Learned Models While Learning Transferable Subtasks—0
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks—0
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning—0
Planning with RL and episodic-memory behavioral priors—0
Planning with Sequence Models through Iterative Energy Minimization—0
Epistemic Monte Carlo Tree Search—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
Plan-Space State Embeddings for Improved Reinforcement Learning—0
Plan Your Target and Learn Your Skills: State-Only Imitation Learning via Decoupled Policy Optimization—0
Plasticity Loss in Deep Reinforcement Learning: A Survey—0
Playing 20 Question Game with Policy-Based Reinforcement Learning—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Playing a Strategy Game with Knowledge-Based Reinforcement Learning—0
Playing Atari Ball Games with Hierarchical Reinforcement Learning—0
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Benchmark Results

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