Policy Gradient Methods
ACTKR
Introduced by Wu et al. in Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximationACKTR, or Actor Critic with Kronecker-factored Trust Region, is an actor-critic method for reinforcement learning that applies trust region optimization using a recently proposed Kronecker-factored approximation to the curvature. The method extends the framework of natural policy gradient and optimizes both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region.
Source: Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Imitation Learning | 1 | 20.00% |
| OpenAI Gym | 1 | 20.00% |
| Atari Games | 1 | 20.00% |
| Continuous Control | 1 | 20.00% |
| reinforcement Learning | 1 | 20.00% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
Convolution
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Convolutions | |
Dense Connections
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Feedforward Networks | |
ELU
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Activation Functions | (optional) |
Entropy Regularization
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Regularization | |
ReLU
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Activation Functions | (optional) |
Softmax
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Output Functions | (optional) |
Tanh Activation
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Activation Functions | (optional) |
