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39 lines
3.4 KiB
Markdown
39 lines
3.4 KiB
Markdown
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# Policy Gradients
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Policy gradients are a popular and powerful technique used in the field of reinforcement learning. They offer a way to optimize the policy of an agent by directly estimating and updating the policy parameters based on the observed rewards.
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## Reinforcement Learning
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To understand policy gradients, it's essential to have a basic understanding of reinforcement learning (RL). In RL, an agent interacts with an environment by taking actions, and the environment provides feedback in the form of rewards or penalties. The goal of the agent is to learn a policy, which is a mapping from states to actions, that maximizes the cumulative reward over time.
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## Direct Policy Optimization
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Policy gradients take a direct optimization approach to finding an optimal policy. Rather than estimating the value function or action-value function, they aim to optimize the policy without intermediate steps. This makes them well-suited for continuous action spaces and tasks with high dimensionality.
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## The Policy Gradient Theorem
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The policy gradient theorem provides the theoretical foundation for policy gradients. It states that the gradient of the expected discounted return with respect to the policy parameters is proportional to the expected sum of the gradients of the log-probabilities of each action multiplied by the corresponding reward.
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In other words, the gradient of the expected return is a sum of gradients of log-probabilities times rewards. This gradient can be used to update the policy parameters in a way that maximizes the expected return.
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## Vanilla Policy Gradient
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The Vanilla Policy Gradient (VPG) algorithm is a simple implementation of policy gradients. It involves estimating gradients using Monte Carlo sampling of trajectories and updating the policy parameters based on these gradients. VPG has shown promising results in various domains, including games and robotics.
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## Advantage Actor-Critic (A2C)
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The Advantage Actor-Critic (A2C) algorithm is an extension of policy gradients that combines the benefits of both value-based and policy-based methods. A2C uses a separate value function to estimate the advantage of each action, which helps in reducing the variance of the gradient estimates.
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By using a value function, A2C provides a baseline and makes the learning process less noisy, resulting in faster and more stable convergence.
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## Proximal Policy Optimization (PPO)
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Proximal Policy Optimization (PPO) is another popular algorithm that uses policy gradients. PPO addresses the issue of overly aggressive policy updates by introducing a surrogate objective function that puts a constraint on the policy divergence.
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PPO iteratively samples multiple trajectories, computes the policy gradient, and performs multiple epochs of optimization updates. This approach results in significantly improved robustness and stability compared to previous methods.
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## Conclusion
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Policy gradients have become a prominent technique in reinforcement learning, enabling direct optimization of policies for a wide range of problems. Algorithms like Vanilla Policy Gradient, Advantage Actor-Critic, and Proximal Policy Optimization provide different approaches to policy optimization, each with their strengths and applications.
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As research progresses, policy gradients are expected to continue evolving and contributing to the advancement of reinforcement learning, opening up new possibilities for autonomous agents in various domains.
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