dqn.rst

DQN

Deep Q-Network (DQN) is an off-policy value-based algorithm for discrete action space. It uses a deep neural network to approximate a Q function defined on state-action pairs. This implementation starts from a vanilla Deep Q-Learning and supports the following extensions:

• Experience replay: ✔️
• Target network (soft update): ✔️
• Gradient clipping: ✔️
• Reward clipping: ❌
• Prioritized Experience Replay (PER): ✔️
• Dueling network architecture: ✔️

Note

This implementation has no support for reward clipping because we introduce the hyper-paramter reward_scale for reward scaling as an alternative. We believe that the clipping function may omit information since it cannot map the clipped reward back to the original reward; however, the reward scaling function is able to manipulate the reward back and forth.

Warning

PER leads to a faster learning speed and is also critical for environments with sparse rewards. However, a replay buffer with small size may hurt the performance of PER.

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.AgentDQN import AgentDQN

# train and save
args = Arguments(env=build_env('CartPole-v0'), agent=AgentDQN())
args.cwd = 'demo_CartPole_DQN'
args.target_return = 195
args.agent.if_use_dueling = True
train_and_evaluate(args) 

# test
agent = AgentDQN()
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()

Parameters

elegantrl.agents.AgentDQN.AgentDQN

Networks

elegantrl.agents.net.QNet

elegantrl.agents.net.QNetDuel