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test_ddpg.py
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test_ddpg.py
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import os
from os.path import dirname
import sys
import gymnasium as gym
import torch
dir_ = dirname(dirname(__file__))
print(dir_)
sys.path.append(dir_)
from RLAlgo.DDPG import DDPG
from RLUtils import train_off_policy, play, Config, gym_env_desc
def ddpg_test():
"""
policyNet:
valueNet:
"""
# env_name = 'Pendulum-v1'
# env = gym.make(env_name)
# Pendulum_cfg = Config(
# env,
# num_episode = 180,
# save_path=r'D:\TMP\ddpg_test_actor.ckpt',
# actor_hidden_layers_dim=[128, 64],
# critic_hidden_layers_dim=[64, 32],
# actor_lr=3e-5,
# critic_lr=5e-4,
# sample_size=256,
# off_buffer_size=2048,
# off_minimal_size=1024,
# max_episode_rewards=2048,
# max_episode_steps=240,
# gamma=0.9,
# DDPG_kwargs={
# 'tau': 0.05, # soft update parameters
# 'sigma': 0.005, # noise
# 'action_bound': 2.0
# }
# )
env_name = 'MountainCarContinuous-v0'
gym_env_desc(env_name)
env = gym.make(env_name)
cfg = Config(
env,
# 环境参数
save_path=r'D:\TMP\ddpg_MountainCarContinuous_test_actor.ckpt',
seed=42,
# 网络参数
actor_hidden_layers_dim=[512, 256],
critic_hidden_layers_dim=[512, 256],
# agent参数
actor_lr=3e-4,
critic_lr=5e-3,
gamma=0.99,
# 训练参数
num_episode=100,
sample_size=512,
off_buffer_size=2048000,
off_minimal_size=2048*5,
max_episode_rewards=90,
max_episode_steps=1500,
# agent 其他参数
DDPG_kwargs={
'tau': 0.005, # soft update parameters
'sigma': 0.8, # noise
'action_bound': 1.1,
'action_low': env.action_space.low[0],
'action_high': env.action_space.high[0],
}
)
agent = DDPG(
state_dim=cfg.state_dim,
actor_hidden_layers_dim=cfg.actor_hidden_layers_dim,
critic_hidden_layers_dim=cfg.critic_hidden_layers_dim,
action_dim=cfg.action_dim,
actor_lr=cfg.actor_lr,
critic_lr=cfg.critic_lr,
gamma=cfg.gamma,
DDPG_kwargs=cfg.DDPG_kwargs,
device=cfg.device
)
# agent.train = True
# train_off_policy(env, agent, cfg, done_add=True)
try:
agent.target_q.load_state_dict(
torch.load(cfg.save_path)
)
except Exception as e:
agent.actor.load_state_dict(
torch.load(cfg.save_path)
)
agent.train = False
play(gym.make(env_name, render_mode='human'), agent, cfg, episode_count=1)
def LunarLanderContinuous_ddpg_test():
"""
policyNet:
valueNet:
"""
env_name = 'LunarLanderContinuous-v2'
gym_env_desc(env_name)
env = gym.make(env_name)
path_ = os.path.dirname(__file__)
cfg = Config(
env,
# 环境参数
save_path=os.path.join(path_, "test_models" ,'ddpg_LunarLanderContinuous-v2_test_actor-3.ckpt'),
seed=42,
# 网络参数
actor_hidden_layers_dim=[128, 128],
critic_hidden_layers_dim=[128, 128],
# agent参数
# actor_lr=5e-4, # 128, 64
# critic_lr=1e-3, # 128, 64
actor_lr=1e-4,
critic_lr=5e-4,
gamma=0.99,
# 训练参数
num_episode=5000,
sample_size=256,
off_buffer_size=165*100, # 环境不是非常复杂
off_minimal_size=1024,
max_episode_rewards=500,
max_episode_steps=180, # 215 step get-299 rewards # 165
# agent 其他参数
DDPG_kwargs={
'tau': 0.001, # soft update parameters
'sigma': 0.15, # noise
'sigma_exp_reduce_factor': 1,
'action_low': env.action_space.low,
'action_high': env.action_space.high,
'off_minimal_size': 1024
}
)
agent = DDPG(
state_dim=cfg.state_dim,
actor_hidden_layers_dim=cfg.actor_hidden_layers_dim,
critic_hidden_layers_dim=cfg.critic_hidden_layers_dim,
action_dim=cfg.action_dim,
actor_lr=cfg.actor_lr,
critic_lr=cfg.critic_lr,
gamma=cfg.gamma,
DDPG_kwargs=cfg.DDPG_kwargs,
device=cfg.device
)
# agent.train = True
# train_off_policy(env, agent, cfg, done_add=False)
try:
agent.target_q.load_state_dict(
torch.load(cfg.save_path)
)
except Exception as e:
agent.actor.load_state_dict(
torch.load(cfg.save_path)
)
agent.train = False
play(gym.make(env_name, render_mode='human'), agent, cfg, episode_count=2)
def BipedalWalker_ddpg_test():
"""
policyNet:
valueNet:
"""
env_name = 'BipedalWalker-v3'
gym_env_desc(env_name)
env = gym.make(env_name)
path_ = os.path.dirname(__file__)
cfg = Config(
env,
# 环境参数
# save_path=r'D:\TMP\ddpg_BipedalWalker-v3_test_actor-v2.ckpt', # 1000次 291
save_path=os.path.join(path_, "test_models" ,'DDPG_BipedalWalker-v3_test_actor-0.ckpt'),
seed=42,
# 网络参数
actor_hidden_layers_dim=[128, 64],
critic_hidden_layers_dim=[128, 64],
# agent参数
actor_lr=5e-5, # 3e-5,
critic_lr=1e-3, # 5e-4
gamma=0.99,
# 训练参数
num_episode=5000,
sample_size=128,
off_buffer_size=20480,
off_minimal_size=2048,
max_episode_rewards=900,
max_episode_steps=300,
# agent 其他参数
DDPG_kwargs={
'tau': 0.05, # soft update parameters
'sigma': 0.5, # noise
'action_bound': 1.0,
'action_low': env.action_space.low[0],
'action_high': env.action_space.high[0],
}
)
agent = DDPG(
state_dim=cfg.state_dim,
actor_hidden_layers_dim=cfg.actor_hidden_layers_dim,
critic_hidden_layers_dim=cfg.critic_hidden_layers_dim,
action_dim=cfg.action_dim,
actor_lr=cfg.actor_lr,
critic_lr=cfg.critic_lr,
gamma=cfg.gamma,
DDPG_kwargs=cfg.DDPG_kwargs,
device=cfg.device
)
agent.train = True
# train_off_policy(env, agent, cfg, done_add=False)
try:
agent.target_q.load_state_dict(
torch.load(cfg.save_path)
)
except Exception as e:
agent.actor.load_state_dict(
torch.load(cfg.save_path)
)
agent.train = False
play(gym.make(env_name, render_mode='human'), agent, cfg, episode_count=2)
def carRacing_ddpg_test():
"""
policyNet:
valueNet:
"""
env_name = 'CarRacing-v2'
gym_env_desc(env_name)
env = gym.make(env_name)
path_ = os.path.dirname(__file__)
cfg = Config(
env,
# 环境参数
save_path=os.path.join(path_, "test_models" ,'DDPG_CarRacing-v2_test_actor-0.ckpt'),
seed=42,
# 网络参数
actor_hidden_layers_dim=[128, 64],
critic_hidden_layers_dim=[128, 64],
# agent参数
actor_lr=5e-5, # 3e-5,
critic_lr=1e-3, # 5e-4
gamma=0.99,
# 训练参数
num_episode=100,
sample_size=128,
off_buffer_size=20480,
off_minimal_size=2048,
max_episode_rewards=900,
max_episode_steps=300,
# agent 其他参数
DDPG_kwargs={
'tau': 0.05, # soft update parameters
'sigma': 0.5, # noise
'sigma_exp_reduce_factor': 0.999,
'action_low': env.action_space.low[0],
'action_high': env.action_space.high[0],
}
)
# 需要调整网络适应于图片输入
agent = DDPG(
state_dim=cfg.state_dim,
actor_hidden_layers_dim=cfg.actor_hidden_layers_dim,
critic_hidden_layers_dim=cfg.critic_hidden_layers_dim,
action_dim=cfg.action_dim,
actor_lr=cfg.actor_lr,
critic_lr=cfg.critic_lr,
gamma=cfg.gamma,
DDPG_kwargs=cfg.DDPG_kwargs,
device=cfg.device
)
agent.train = True
train_off_policy(env, agent, cfg, done_add=False)
try:
agent.target_q.load_state_dict(
torch.load(cfg.save_path)
)
except Exception as e:
agent.actor.load_state_dict(
torch.load(cfg.save_path)
)
agent.train = False
play(gym.make(env_name, render_mode='human'), agent, cfg, episode_count=2)
if __name__ == '__main__':
LunarLanderContinuous_ddpg_test()
# BipedalWalker_ddpg_test()
# carRacing_ddpg_test()