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Reinforcement Learning: Deep Deterministic Policy Gradient #3494
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zoq
reviewed
Jun 11, 2023
Signed-off-by: Tarek <tareknaser360@gmail.com>
Signed-off-by: Tarek <tareknaser360@gmail.com>
Signed-off-by: Tarek <tareknaser360@gmail.com>
Signed-off-by: Tarek <tareknaser360@gmail.com>
zoq
reviewed
Jun 14, 2023
Signed-off-by: Tarek <tareknaser360@gmail.com>
zoq
approved these changes
Jun 16, 2023
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No more comments from my side, awesome work.
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Second approval provided automatically after 24 hours. 👍
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Description
This pull request implements the DDPG (Deep Deterministic Policy Gradient) algorithm, along with 2 test cases.
Implementation details
DDPG is an actor-critic algorithm designed for continuous action spaces. It combines deep neural networks with deterministic policy gradients to learn optimal policies in a continuous control setting.
Implemented four networks:
policyNetwork
(actor network)targetPNetwork
(target actor network)learningQNetwork
(critic network)targetQNetwork
(target critic network)How Has This Been Tested?
The test configurations for DDPG are adapted from the SAC (Soft Actor-Critic) implementation since both DDPG and SAC are policy gradient off-policy algorithms. This ensures consistent evaluation and comparison of the algorithms.