Double DQN¶
Double Deep Q-Network (Double DQN) is one of the most important extensions of vanilla DQN. It resolves the issue of overestimation via a simple trick: decoupling the max operation in the target into action selection and action evaluation.
Without having to introduce additional networks, we use a Q-network to select the best among the available next actions and use the target network to evaluate its Q-value. This implementation supports the following extensions:
Experience replay: ✔️
Target network: ✔️
Gradient clipping: ✔️
Reward clipping: ❌
Prioritized Experience Replay (PER): ✔️
Dueling network architecture: ✔️
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.AgentDoubleDQN import AgentDoubleDQN
# train and save
args = Arguments(env=build_env('CartPole-v0'), agent=AgentDoubleDQN())
args.cwd = 'demo_CartPole_DoubleDQN'
args.target_return = 195
train_and_evaluate(args)
# test
agent = AgentDoubleDQN()
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('CartPole-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()