Review:
Policy Gradient Methods
overall review score: 4
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score is between 0 and 5
Policy gradient methods are a class of reinforcement learning algorithms that directly optimize the policy of an agent.
Key Features
- Directly optimize policy
- Used in reinforcement learning
- Can handle large action spaces
Pros
- Effective in handling problems with large action spaces
- Can handle continuous action spaces
- Can learn stochastic policies
Cons
- High variance in gradient estimates
- Can be computationally expensive
- Sensitive to hyperparameters