Reinforcement learning
How an agent learns to act from rewards.
- Markov decision processes
- Dynamic programming in RL
- Monte Carlo and TD learning
- Function approximation in RL
- Deep Q-learning
- Policy gradient methods
- Actor-critic methods
- Model-based RL
- Exploration
- Multi-armed bandits
- Contextual bandits
- Offline RL
- Imitation learning
- Inverse RL and reward learning
- Hierarchical RL
- Multi-agent RL
- Partially observable RL
- RL theory
- Reward design and shaping
- RL environments and benchmarks
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