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Methods
Policy based
Policy gradient
REINFORCE (Vanilla)
TRPO
PPO
A2C
A3C
Generalized Advantage
Estimation (GAE)
TRPO Kronecker-factored
(ACKTR)
ACER
SAC
Deterministic Policy Gradients
DPG
DDPG
TD3
Evolutionary
CEM
CMA-ES
PEPG
NES
ES
Policy search
PILCO
REPS
Model-based
Pure planning
Expert Iteration
Data Augmentation
Embedding planning loops into
policies
AIXI
Imitation learning
DAGGER
GAIL
DeepMimic
VAIL
MetaMimic
Value based
Implementations
Q-Learning
Sarsa
Dyna
DDPG
Algorithm space
Full backups vs sample backups
(tree width)
Shallow backups vs deep
backups (tree depth)
Advantages
More data efficient than policy
based RL
Disadvantages
High dimensional / continuous
action spaces
Stochastic policies
http://louiskirsch.com

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methods.pdf

  • 1. Methods Policy based Policy gradient REINFORCE (Vanilla) TRPO PPO A2C A3C Generalized Advantage Estimation (GAE) TRPO Kronecker-factored (ACKTR) ACER SAC Deterministic Policy Gradients DPG DDPG TD3 Evolutionary CEM CMA-ES PEPG NES ES Policy search PILCO REPS Model-based Pure planning Expert Iteration Data Augmentation Embedding planning loops into policies AIXI Imitation learning DAGGER GAIL DeepMimic VAIL MetaMimic Value based Implementations Q-Learning Sarsa Dyna DDPG Algorithm space Full backups vs sample backups (tree width) Shallow backups vs deep backups (tree depth) Advantages More data efficient than policy based RL Disadvantages High dimensional / continuous action spaces Stochastic policies http://louiskirsch.com