DDPG with Hindsight Experience Replay
GPTKB entity
Statements (53)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
software
|
| gptkbp:appliesTo |
Continuous_Action_Spaces
|
| gptkbp:canLeadTo |
Overfitting
Simulated Environments Real-World Tasks |
| gptkbp:developedBy |
gptkb:Google_DeepMind
|
| gptkbp:enhances |
Exploration Strategies
|
| https://www.w3.org/2000/01/rdf-schema#label |
DDPG with Hindsight Experience Replay
|
| gptkbp:improves |
Sample Efficiency
Learning from Failures |
| gptkbp:isBasedOn |
Deterministic_Policy_Gradient
|
| gptkbp:isBeneficialFor |
Sparse Reward Problems
Delayed Reward Problems |
| gptkbp:isEvaluatedBy |
gptkb:Atari_Games
Convergence Speed Success Rate OpenAI Gym Average Reward Continuous_Control_Tasks |
| gptkbp:isExploredIn |
Conferences
Workshops Academic Papers |
| gptkbp:isInfluencedBy |
Q-Learning
Actor-Critic Methods Policy_Gradient_Methods |
| gptkbp:isLocatedIn |
gptkb:PyTorch
TensorFlow |
| gptkbp:isPartOf |
gptkb:TRPO
SAC PPO |
| gptkbp:isPopularIn |
Research_Community
|
| gptkbp:isRelatedTo |
gptkb:Deep_Reinforcement_Learning
|
| gptkbp:isSupportedBy |
Research Grants
Collaborative Projects Open Source Libraries |
| gptkbp:isUsedFor |
gptkb:Hindsight_Experience_Replay
gptkb:DDPG Action Selection Value Function Approximation Policy Optimization |
| gptkbp:isUsedIn |
Robotics
Game Playing |
| gptkbp:isUtilizedFor |
High Dimensionality
Ensure Generalization Handle Non-Stationary Environments Manage Computational Resources Stabilize Training Balance_Exploration_and_Exploitation Continuous_Action_Spaces |
| gptkbp:requires |
Neural Networks
Hyperparameters |
| gptkbp:uses |
Actor-Critic Architecture
|
| gptkbp:utilizes |
Experience_Replay_Buffer
|