Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking

15 Nov 2020 • Fabio Pardo

Deep reinforcement learning has been one of the fastest growing fields of machine learning over the past years and numerous libraries have been open sourced to support research. However, most codebases have a steep learning curve or limited flexibility that do not satisfy a need for fast prototyping in fundamental research... (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
Convolution	Convolutions
Clipped Double Q-learning	Off-Policy TD Control
ReLU	Activation Functions
Target Policy Smoothing	Regularization
Weight Decay	Regularization
Batch Normalization	Normalization
Entropy Regularization	Regularization
Prioritized Experience Replay	Replay Memory
Dense Connections	Feedforward Networks
DDPG	Policy Gradient Methods
N-step Returns	Value Function Estimation
Experience Replay	Replay Memory
Adam	Stochastic Optimization
TD3	Policy Gradient Methods
D4PG	Policy Gradient Methods
A2C	Policy Gradient Methods
PPO	Policy Gradient Methods
TRPO	Policy Gradient Methods

METHOD

TYPE

Dilated Convolution