Algorithms

AgileRL already includes state-of-the-art evolvable on-policy, off-policy, offline and multi-agent reinforcement learning algorithms with distributed training. We are constantly adding more algorithms, with a view to add hierarchical algorithms soon.

Reinforcement learning algorithms implemented:

• Conservative Q-Learning (CQL)
• Deep Deterministic Policy Gradient (DDPG)
• Deep Q-Learning (DQN)
• Rainbow DQN
• Implicit Language Q-Learning (ILQL)
• Proximal Policy Optimization (PPO)
• Twin Delayed Deep Deterministic Policy Gradient (TD3)
• Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
• Multi-Agent Twin-Delayed Deep Deterministic Policy Gradient (MATD3)
• Neural Contextual Bandits with UCB-based Exploration (NeuralUCB)
• Neural Contextual Bandits with Thompson Sampling (NeuralTS)