Policy Gradient RL Algorithms as Directed Acyclic Graphs

14 Dec 2020 • Juan Jose Garau Luis

Meta Reinforcement Learning (RL) methods focus on automating the design of RL algorithms that generalize to a wide range of environments. The framework introduced in (Anonymous, 2020) addresses the problem by representing different RL algorithms as Directed Acyclic Graphs (DAGs), and using an evolutionary meta learner to modify these graphs and find good agent update rules... (read more)

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METHOD	TYPE
Dilated Convolution	Convolutions
Global Average Pooling	Pooling Operations
Average Pooling	Pooling Operations
1x1 Convolution	Convolutions
SAC	Convolutions
Batch Normalization	Normalization
Adam	Stochastic Optimization
Experience Replay	Replay Memory
ReLU	Activation Functions
Convolution	Convolutions
Q-Learning	Off-Policy TD Control
Clipped Double Q-learning	Off-Policy TD Control
Target Policy Smoothing	Regularization
TD3	Policy Gradient Methods
Weight Decay	Regularization
Dense Connections	Feedforward Networks
Entropy Regularization	Regularization
DDPG	Policy Gradient Methods
DQN	Q-Learning Networks
PPO	Policy Gradient Methods

METHOD

TYPE

Dilated Convolution