SAC¶
Soft Actor-Critic (SAC) is an off-policy Actor-Critic algorithm for continuous action space. In SAC, it introduces an entropy regularization to the loss function, which has a close connection with the trade-off of the exploration and exploitation. In our implementation, we employ a learnable entropy regularization coefficienct to dynamic control the scale of the entropy, which makes it consistent with a pre-defined target entropy. SAC also utilizes Clipped Double-Q Learning (mentioned in TD3) to overcome the overestimation of Q-values. This implementation provides SAC and supports the following extensions:
Experience replay: ✔️
Target network: ✔️
Gradient clipping: ✔️
Reward clipping: ❌
Prioritized Experience Replay (PER): ✔️
Leanable entropy regularization coefficient: ✔️
Note
Inspired by the delayed policy update from TD3, we implement a modified version of SAC AgentModSAC with a dynamic adjustment of the frequency of the policy update. The adjustment is based on the loss of critic networks: a small loss leads to a high update frequency and vise versa.
Code Snippet¶
import torch
from elegantrl.run import train_and_evaluate
from elegantrl.config import Arguments
from elegantrl.train.config import build_env
from elegantrl.agents.AgentSAC import AgentSAC
# train and save
args = Arguments(env=build_env('Pendulum-v0'), agent=AgentSAC())
args.cwd = 'demo_Pendulum_SAC'
args.env.target_return = -200
args.reward_scale = 2 ** -2
train_and_evaluate(args)
# test
agent = AgentSAC()
agent.init(args.net_dim, args.state_dim, args.action_dim)
agent.save_or_load_agent(cwd=args.cwd, if_save=False)
env = build_env('Pendulum-v0')
state = env.reset()
episode_reward = 0
for i in range(2 ** 10):
action = agent.select_action(state)
next_state, reward, done, _ = env.step(action)
episode_reward += reward
if done:
print(f'Step {i:>6}, Episode return {episode_reward:8.3f}')
break
else:
state = next_state
env.render()