Policy Gradient Methods

Deep Deterministic Policy Gradient

Introduced by Lillicrap et al. in Continuous control with deep reinforcement learning

DDPG, or Deep Deterministic Policy Gradient, is an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. It combines the actor-critic approach with insights from DQNs: in particular, the insights that 1) the network is trained off-policy with samples from a replay buffer to minimize correlations between samples, and 2) the network is trained with a target Q network to give consistent targets during temporal difference backups. DDPG makes use of the same ideas along with batch normalization.

Source: Continuous control with deep reinforcement learning

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Continuous Control 22 30.99%
OpenAI Gym 10 14.08%
Decision Making 5 7.04%
Autonomous Driving 4 5.63%
Meta-Learning 3 4.23%
Motion Planning 3 4.23%
Efficient Exploration 3 4.23%
Imitation Learning 3 4.23%
Unity 2 2.82%

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