API Reference - Models
| Model Type | Description | Count |
|---|---|---|
| Deep Reinforcement Learning | State-Action Optimization Using Neural Networks | 26 |
| Generative | Feature To Novel Value | 4 |
| Others | 1 | |
| Total | 31 |
Legend
| Icon | Name | Description |
|---|---|---|
| ❗ | Implementation Issue | The model may have some implementation problems. |
| 🔰 | Beginner Algorithm | Commonly taught to beginners. |
| 💾 | Data Efficient | Require few data to train the model. |
| ⚡ | Computationally Efficient | Require few computational resources to train the model. |
| 🛡️ | Noise Resistant | Can handle randomness / unclean data. |
| 🟢 | Online | Can adapt real-time. |
| 🟡 | Session-Adaptive / Offline | Can be retrained each session. |
| ⚠️ | Assumption-Heavy | Have restrictive rules on using the model. |
| ⚙️ | Configuration-Heavy | Requires a lot of manual configuration to use. |
Deep Reinforcement Learning
❗Implementation Issue 🔰 Beginner Algorithm 💾 Data Efficient ⚡ Computationally Efficient 🛡️ Noise Resistant 🟢 Online 🟡 Session-Adaptive / Offline ⚠️ Assumption-Heavy ⚙️ Configuration-Heavy
| Model | Alternate Names | Properties | Use Cases |
|---|---|---|---|
| DeepQLearning | Deep Q Network | 💾 🟢 | Best Self-Learning Player AIs, Best Recommendation Systems |
| DeepNStepQLearning | Deep N-Step Q Network | 💾 🟢 | Best Self-Learning Player AIs, Best Recommendation Systems |
| DeepDoubleQLearningV1 | Double Deep Q Network (2010) | 💾 🛡️ 🟢 | Stable Best Self-Learning Player AIs, Best Recommendation Systems |
| DeepDoubleQLearningV2 | Double Deep Q Network (2015) | 💾 🛡️ 🟢 | Stable Best Self-Learning Player AIs, Best Recommendation Systems |
| DeepClippedDoubleQLearning | Clipped Deep Double Q Network | 💾 🛡️ 🟢 | Stable Best Self-Learning Player AIs, Best Recommendation Systems |
| DeepStateActionRewardStateAction | Deep SARSA | 🟢 | Safe Self-Learning Player AIs, Safe Recommendation Systems |
| DeepNStepStateActionRewardStateAction | Deep N-Step SARSA | 🟢 | Safe Self-Learning Player AIs, Safe Recommendation Systems |
| DeepDoubleStateActionRewardStateActionV1 | Double Deep SARSA | 🛡️ 🟢 | Stable Safe Self-Learning Player AIs, Safe Recommendation Systems |
| DeepDoubleStateActionRewardStateActionV2 | Double Deep SARSA | 🛡️ 🟢 | Stable Safe Self-Learning Player AIs, Safe Recommendation Systems |
| DeepExpectedStateActionRewardStateAction | Deep Expected SARSA | 🟢 | Balanced Self-Learning Player AIs, Balanced Recommendation Systems |
| DeepNStepExpectedStateActionRewardStateAction | Deep N-Step Expected SARSA | 🟢 | Balanced Self-Learning Player AIs, Balanced Recommendation Systems |
| DeepDoubleExpectedStateActionRewardStateActionV1 | Double Deep Expected SARSA | 🛡️ 🟢 | Stable Balanced Self-Learning Player AIs, Balanced Recommendation Systems |
| DeepDoubleExpectedStateActionRewardStateActionV2 | Double Deep Expected SARSA | 🛡️ 🟢 | Stable Balanced Self-Learning Player AIs, Balanced Recommendation Systems |
| MonteCarloControl | None | ❗ 🟢 | Online Self-Learning Player AIs |
| OffPolicyMonteCarloControl | None | 🟢 | Offline Self-Learning Player AIs |
| DeepTemporalDifference | TD | 🟢 | Priority Systems |
| REINFORCE | None | 🟢 | Reward-Based Self-Learning Player AIs |
| VanillaPolicyGradient | VPG | ❗ 🟢 | Baseline-Based Self-Learning Player AIs |
| ActorCritic | AC | 🟢 | Critic-Based Self-Learning Player AIs |
| AdvantageActorCritic | A2C | 🟢 | Advantage-Based Self-Learning Player AIs |
| TemporalDifferenceActorCritic | TD-AC | 🟢 | Bootsrapped Online Self-Learning Player AIs |
| ProximalPolicyOptimization | PPO | 🟢 | Industry-Grade And Research-Grade Self-Learning Player And Vehicle AIs |
| ProximalPolicyOptimizationClip | PPO-Clip | 🟢 | Industry-Grade And Research-Grade Self-Learning Player And Vehicle AIs |
| SoftActorCritic | SAC | 💾 🛡️ 🟢 | Self-Learning Vehicle AIs |
| DeepDeterministicPolicyGradient | DDPG | 🟢 ⚙️ | Self-Learning Vehicle AIs |
| TwinDelayedDeepDeterministicPolicyGradient | TD3 | 🟢 🛡️ ⚙️ | Self-Learning Vehicle AIs |
Generative
❗Implementation Issue 🔰 Beginner Algorithm 💾 Data Efficient ⚡ Computationally Efficient 🛡️ Noise Resistant 🟢 Online 🟡 Session-Adaptive / Offline ⚠️ Assumption-Heavy ⚙️ Configuration-Heavy
| Model | Alternate Names | Properties | Use Cases |
|---|---|---|---|
| Diffusion | None | 🟢 🟡 | Building And Image Generation |
| GenerativeAdversarialNetwork | GAN | 🟢 🟡 | Enemy Data Generation |
| ConditionalGenerativeAdversarialNetwork | CGAN | 🟢 🟡 | Conditional Enemy Data Generation |
| WassersteinGenerativeAdversarialNetwork | WGAN | 🟢 🟡 | Stable Enemy Data Generation |
| ConditionalWassersteinGenerativeAdversarialNetwork | CWGAN | 🟢 🟡 | Stable Conditional Enemy Data Generation |
Others
❗Implementation Issue 🔰 Beginner Algorithm 💾 Data Efficient ⚡ Computationally Efficient 🛡️ Noise Resistant 🟢 Online 🟡 Session-Adaptive / Offline ⚠️ Assumption-Heavy ⚙️ Configuration-Heavy
| Model | Alternate Names | Properties | Use Cases |
|---|---|---|---|
| RandomNetworkDistillation | RND | 🟢 🟡 | Intrinsic Reward Generation |