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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Methods used in the Paper


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