Description

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Reimplementation of Soft Actor-Critic Algorithms and Applications and a deterministic variant of SAC from Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Added another branch for Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor -> SAC_V

Requirements

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• mujoco-py
• TensorboardX
• PyTorch

Run

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(Note: There is no need for setting Temperature(--alpha) if --automatic_entropy_tuning is True.)

For SAC :

python main.py --env-name Humanoid-v2 --aplha 0.05

For SAC (Hard Update):

python main.py --env-name Humanoid-v2 --alpha 0.05 --tau 1 --target_update_interval 1000

For SAC (Deterministic, Hard Update):

python main.py --env-name Humanoid-v2 --policy Deterministic --tau 1 --target_update_interval 1000

Default Parameters

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Parameters	Value
Shared	-
optimizer	Adam
learning rate(--lr)	3x10−4
discount(--gamma) (γ)	0.99
replay buffer size(--replay_size)	1x106
automatic_entropy_tuning(--automatic_entropy_tuning)	False
number of hidden layers (all networks)	2
number of hidden units per layer(--hidden_size)	256
number of samples per minibatch(--batch_size)	256
nonlinearity	ReLU
SAC	-
target smoothing coefficient(--tau) (τ)	0.005
target update interval(--target_update_interval)	1
gradient steps(--updates_per_step)	1
SAC (Hard Update)	-
target smoothing coefficient(--tau) (τ)	1
target update interval(--target_update_interval)	1000
gradient steps (except humanoids)(--updates_per_step)	4
gradient steps (humanoids)(--updates_per_step)	1

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Environment (--env-name)	Temperature (--alpha)
HalfCheetah-v2	0.2
Hopper-v2	0.2
Walker2d-v2	0.2
Ant-v2	0.2
Humanoid-v2	0.05

pytorch-ppo-sac-HalfCheetah-v2