Description

---

Reimplementation of Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

Requirements

---

• mujoco-py
• TensorboardX
• PyTorch

Run

---

For SAC :

python main.py --env-name Humanoid-v2 --alpha 0.025 

For SAC (Hard Update):

python main.py --env-name Humanoid-v2 --alpha 0.025 --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

Parameters

---

Parameters	Value
Shared	-
optimizer	Adam
learning rate(--lr)	1x10−3
discount(--gamma) (γ)	0.99
replay buffer size(--replay_size)	1x106
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

Environment (--env-name)	Temperature (--alpha)
HalfCheetah-v2	0.1
Hopper-v2	0.1
Walker2d-v2	0.1
Ant-v2	0.1
Humanoid-v2	0.025