Core Concepts
Distributed Distributional DrQ is a model-free and off-policy reinforcement learning algorithm that uses a distributional perspective on the critic value function to improve the stability and performance of continuous control tasks.
Abstract
Distributed Distributional DrQ is an off-policy, model-free actor-critic reinforcement learning algorithm that builds upon the Distributed Distributional DDPG (D4PG) algorithm. The key aspects of Distributed Distributional DrQ are:
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Data Preprocessing:
- Uses an auto-encoder to encode the visual input into a low-dimensional latent space.
- Applies data augmentation techniques like random shifts and crops to increase data efficiency.
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Distributional Critic Value Function:
- Represents the value function as a categorical distribution over returns, providing more information than a single expected value.
- Uses the distributional Bellman operator to update the critic value function, which is more stable and accurate than the standard Bellman operator.
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Distributed Actor Policy:
- Updates the actor policy by maximizing the expected value of the distributional critic function.
- This distributional perspective on the value function makes the policy gradient method more robust and less sensitive to hyperparameter tuning.
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Algorithmic Improvements:
- Incorporates double Q-learning to mitigate overestimation bias in the critic value function.
- Uses n-step returns to improve the reward propagation and stability of the learning process.
The Distributed Distributional DrQ algorithm aims to achieve better performance and robustness in challenging continuous control tasks compared to the standard DDPG-based approaches, at the cost of increased computational complexity.
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