Artificial Intelligence Learns to Walk with Actor Critic Deep Reinforcement Learning | TD3 Tutorial | Summary and Q&A

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February 8, 2021
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Machine Learning with Phil
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Artificial Intelligence Learns to Walk with Actor Critic Deep Reinforcement Learning | TD3 Tutorial

TL;DR

TD3 is an algorithm designed to address overestimation bias in continuous action space actor-critic methods, using deep neural networks for function approximation.

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Questions & Answers

Q: What is overestimation bias in continuous action space actor-critic methods?

Overestimation bias refers to the tendency of agents to incorrectly estimate the value of a state on the high end. This can lead to a sub-optimal policy as the agent may choose less profitable states.

Q: How does TD3 handle overestimation bias?

TD3 uses two critic networks and takes the minimum value of both critics' outputs as the target value. This helps to reduce overestimation bias. Additionally, TD3 uses target networks and a delayed update rule to further mitigate overestimation bias.

Q: What are the key components of TD3?

The key components of TD3 include using twin critic networks, delayed policy updates, target networks for both the actor and critics, and a soft update rule for target network weights.

Q: How does TD3 address approximation errors in function approximation methods?

Approximation errors in function approximation methods, such as deep neural networks, can lead to overestimation of state values. TD3 handles this by introducing clipped noise to the critic network inputs and using the minimum value between the two critics as the target value.

Summary & Key Takeaways

  • TD3 is an algorithm that addresses overestimation bias in continuous action space actor-critic methods, caused by incorrect estimation of state values.

  • Overestimation bias in TD3 comes from both inherent approximation errors in function approximation methods, such as deep neural networks, and natural variation in rewards.

  • TD3 uses two critic networks and a delayed update rule to handle overestimation bias, along with target networks and soft updates.

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