def demo4_bullet_mujoco_on_policy():
args = Arguments(if_on_policy=True) # hyper-parameters of on-policy is different from off-policy
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
"TotalStep: 1e5, TargetReturn: 18, UsedTime: 3ks, ReacherBulletEnv-v0, PPO"
"TotalStep: 1e6, TargetReturn: 18, UsedTime: 30ks, ReacherBulletEnv-v0, PPO"
args.env = PreprocessEnv(gym.make('ReacherBulletEnv-v0'))
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.break_step = int(2e5 * 8)
args.reward_scale = 2 ** 0 # RewardRange: -15 < 0 < 18 < 25
args.gamma = 0.96
args.eval_times1 = 2 ** 2
args.eval_times1 = 2 ** 5
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
"TotalStep: 3e6, TargetReturn: 1500, UsedTime: 2ks, AntBulletEnv-v0, PPO"
"TotalStep: 10e6, TargetReturn: 2500, UsedTime: 6ks, AntBulletEnv-v0, PPO"
"TotalStep: 46e6, TargetReturn: 3017, UsedTime: 25ks, AntBulletEnv-v0, PPO"
"TotalStep: 5e6, TargetReturn: 1500, UsedTime: 3ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 15e6, TargetReturn: 2500, UsedTime: 10ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 60e6, TargetReturn: 2949, UsedTime: 34ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 2e6, TargetReturn: 1500, UsedTime: 2ks, AntBulletEnv-v0, PPO if_use_cn"
"TotalStep: 10e6, TargetReturn: 2500, UsedTime: 7ks, AntBulletEnv-v0, PPO if_use_cn"
"TotalStep: 53e6, TargetReturn: 2834, UsedTime: 35ks, AntBulletEnv-v0, PPO if_use_cn"
args.env = PreprocessEnv(env=gym.make('AntBulletEnv-v0'))
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.agent.lambda_entropy = 0.05
args.agent.lambda_gae_adv = 0.97
args.if_allow_break = False
args.break_step = int(8e6 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)
args.max_memo = args.env.max_step * 4
args.batch_size = 2 ** 11 # 10
args.repeat_times = 2 ** 3
args.eval_gap = 2 ** 8 # for Recorder
args.eva_size1 = 2 ** 1 # for Recorder
args.eva_size2 = 2 ** 3 # for Recorder
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo2_continuous_action_space_on_policy():
import elegantrl.agent as agent
"""DEMO 2.1: Continuous action env (on-policy)"""
args = Arguments(if_on_policy=True) # hyper-parameters of on-policy is different from off-policy
args.agent = agent.AgentGaePPO() # AgentPPO()
'''choose environment'''
# env = gym.make('Pendulum-v0')
# env.target_reward = -200 # set target_reward manually for env 'Pendulum-v0'
# args.env = PreprocessEnv(env=env)
# args.reward_scale = 2 ** -3 # RewardRange: -1800 < -200 < -50 < 0
# # TotalStep: 4e5, TargetReward: -200, UsedTime: 400s
args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
"TotalStep: 8e5, TargetReward: 200, UsedTime: 1500s"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.gamma = 0.96
# "TotalStep: 8e5, TargetReward: 300, UsedTime: 1800s"
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate__multiprocessing(args)
def demo2_continuous_action_space_off_policy():
import elegantrl.agent as agent
"""DEMO 2.1: Continuous action env (off-policy)"""
args = Arguments(if_on_policy=False)
args.agent = agent.AgentModSAC() # AgentSAC(), AgentTD3(), AgentDDPG()
'''choose environment'''
# env = gym.make('Pendulum-v0')
# env.target_reward = -200 # set target_reward manually for env 'Pendulum-v0'
# args.env = PreprocessEnv(env=env)
# args.reward_scale = 2 ** -3 # RewardRange: -1800 < -200 < -50 < 0
# "TotalStep: 4e5, TargetReward: -200, UsedTime: 400s"
#
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
# "TotalStep: 9e4, TargetReward: 200, UsedTime: 2500s"
args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
args.reward_scale = 2 ** -1 # RewardRange: -200 < -150 < 300 < 334
args.gamma = 0.95
"TotalStep: 2e5, TargetReward: 300, UsedTime: 3500s"
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 2
train_and_evaluate__multiprocessing(args)
def demo2_continuous_action_space_on_policy():
args = Arguments(
if_on_policy=True
) # hyper-parameters of on-policy is different from off-policy
'''choose an DRL algorithm'''
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
'''choose environment'''
"PPO TotalStep: 4e5, TargetReward: -200, UsedTime: 400s, Pendulum-v0"
env = gym.make('Pendulum-v0')
env.target_reward = -200 # set target_reward manually for env 'Pendulum-v0'
args.env = PreprocessEnv(env=env)
args.reward_scale = 2**-3 # RewardRange: -1800 < -200 < -50 < 0
"PPO TotalStep: 8e5, TargetReward: 200, UsedTime: 1500s, LunarLanderContinuous-v2"
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
"PPO TotalStep: 8e5, TargetReward: 300, UsedTime: 1800s, BipedalWalker-v3"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.gamma = 0.96
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo2_continuous_action_space_off_policy():
args = Arguments(if_on_policy=False)
'''choose an DRL algorithm'''
from elegantrl.agent import AgentModSAC # AgentSAC, AgentTD3, AgentDDPG
args.agent = AgentModSAC()
'''choose environment'''
"TD3 TotalStep: 3e4, TargetReward: -200, UsedTime: 300s, Pendulum-v0"
"ModSAC TotalStep: 4e4, TargetReward: -200, UsedTime: 400s, Pendulum-v0"
env = gym.make('Pendulum-v0')
env.target_reward = -200 # set target_reward manually for env 'Pendulum-v0'
args.env = PreprocessEnv(env=env)
args.reward_scale = 2**-3 # RewardRange: -1800 < -200 < -50 < 0
"TD3 TotalStep: 9e4, TargetReward: 100, UsedTime: 3ks, LunarLanderContinuous-v2"
"TD3 TotalStep: 20e4, TargetReward: 200, UsedTime: 5ks, LunarLanderContinuous-v2"
"SAC TotalStep: 9e4, TargetReward: 200, UsedTime: 3ks, LunarLanderContinuous-v2"
"ModSAC TotalStep: 5e4, TargetReward: 200, UsedTime: 1ks, LunarLanderContinuous-v2"
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
# args.eval_times2 = 2 ** 4 # set a large eval_times to get a precise learning curve
"ModSAC TotalStep: 2e5, TargetReward: 300, UsedTime: 5000s, BipedalWalker-v3"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.net_dim = 2 ** 8
# args.break_step = int(2e5)
# args.if_allow_break = True # allow break training when reach goal (early termination)
# args.break_step = int(2e5 * 4) # break training after 'total_step > break_step'
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo1_discrete_action_space():
args = Arguments(agent=None, env=None, gpu_id=None) # see Arguments() to see hyper-parameters
'''choose an DRL algorithm'''
# from elegantrl.agent import AgentD3QN # AgentDQN,AgentDuelDQN, AgentDoubleDQN,
# args.agent = AgentD3QN()
from elegantrl.agent import AgentDuelingDQN # AgentDQN,AgentDuelDQN, AgentDoubleDQN,
args.agent = AgentDuelingDQN()
'''choose environment'''
"TotalStep: 2e3, TargetReturn: 200, UsedTime: 20s, CartPole-v0"
"TotalStep: 2e3, TargetReturn: 200, UsedTime: 30s, CartPole-v0 rollout_num = 2"
# args.env = PreprocessEnv(env=gym.make('CartPole-v0'))
# args.net_dim = 2 ** 7 # change a default hyper-parameters
# args.batch_size = 2 ** 7
# args.target_step = 2 ** 8
# args.eval_gap = 2 ** 0
"TotalStep: 6e4, TargetReturn: 200, UsedTime: 600s, LunarLander-v2, D3DQN"
"TotalStep: 4e4, TargetReturn: 200, UsedTime: 600s, LunarLander-v2, DuelDQN"
args.env = PreprocessEnv(env=gym.make('LunarLander-v2'))
args.net_dim = 2 ** 8
args.batch_size = 2 ** 8
'''train and evaluate'''
train_and_evaluate(args)
def demo4_bullet_mujoco_off_policy():
args = Arguments(if_on_policy=False)
args.random_seed = 10086
from elegantrl.agent import AgentModSAC # AgentSAC, AgentTD3, AgentDDPG
args.agent = AgentModSAC() # AgentSAC(), AgentTD3(), AgentDDPG()
args.agent.if_use_dn = True
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
"TotalStep: 5e4, TargetReturn: 18, UsedTime: 1100s, ReacherBulletEnv-v0"
"TotalStep: 30e4, TargetReturn: 25, UsedTime: s, ReacherBulletEnv-v0"
args.env = PreprocessEnv(gym.make('ReacherBulletEnv-v0'))
args.env.max_step = 2 ** 10 # important, default env.max_step=150
args.reward_scale = 2 ** 0 # -80 < -30 < 18 < 28
args.gamma = 0.96
args.break_step = int(6e4 * 8) # (4e4) 8e5, UsedTime: (300s) 700s
args.eval_times1 = 2 ** 2
args.eval_times1 = 2 ** 5
args.if_per = True
train_and_evaluate(args)
"TotalStep: 3e5, TargetReward: 1500, UsedTime: 4ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 4e5, TargetReward: 2500, UsedTime: 6ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 10e5, TargetReward: 2879, UsedTime: ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 3e5, TargetReward: 1500, UsedTime: 8ks, AntBulletEnv-v0 ModSAC if_use_cn"
"TotalStep: 7e5, TargetReward: 2500, UsedTime: 18ks, AntBulletEnv-v0 ModSAC if_use_cn"
"TotalStep: 16e5, TargetReward: 2923, UsedTime: ks, AntBulletEnv-v0 ModSAC if_use_cn"
args.env = PreprocessEnv(env=gym.make('AntBulletEnv-v0'))
args.break_step = int(6e5 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.if_allow_break = False
args.reward_scale = 2 ** -2 # RewardRange: -50 < 0 < 2500 < 3340
args.max_memo = 2 ** 21
args.batch_size = 2 ** 8
args.repeat_times = 2 ** 1
args.eval_gap = 2 ** 9 # for Recorder
args.eva_size1 = 2 ** 1 # for Recorder
args.eva_size2 = 2 ** 3 # for Recorder
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo4_bullet_mujoco_on_policy():
args = Arguments(
if_on_policy=True
) # hyper-parameters of on-policy is different from off-policy
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
"TotalStep: 1e5, TargetReward: 18, UsedTime: 3ks, ReacherBulletEnv-v0"
"TotalStep: 1e6, TargetReward: 18, UsedTime: 30ks, ReacherBulletEnv-v0"
args.env = PreprocessEnv(gym.make('ReacherBulletEnv-v0'))
args.break_step = int(2e5 * 8)
args.reward_scale = 2**0 # RewardRange: -15 < 0 < 18 < 25
args.gamma = 0.96
args.eval_times1 = 2**2
args.eval_times1 = 2**5
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
"TotalStep: 2e6, TargetReward: 1500, UsedTime: 3ks, AntBulletEnv-v0"
"TotalStep: 13e6, TargetReward: 2400, UsedTime: 21ks, AntBulletEnv-v0"
args.env = PreprocessEnv(env=gym.make('AntBulletEnv-v0'))
args.env.max_step = 2**10
args.break_step = int(2e6 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.reward_scale = 2**-2 # (-50) 0 ~ 2500 (3340)
args.max_memo = 2**11
args.repeat_times = 2**3
args.batch_size = 2**10
args.net_dim = 2**9
args.show_gap = 2**8 # for Recorder
args.eva_size1 = 2**1 # for Recorder
args.eva_size2 = 2**3 # for Recorder
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo1_discrete_action_space():
args = Arguments(agent=None, env=None,
gpu_id=None) # see Arguments() to see hyper-parameters
'''choose an DRL algorithm'''
from elegantrl.agent import AgentD3QN # AgentDQN,AgentDuelDQN, AgentDoubleDQN,
args.agent = AgentD3QN()
'''choose environment'''
# args.env = PreprocessEnv(env=gym.make('CartPole-v0'))
# args.net_dim = 2 ** 7 # change a default hyper-parameters
# args.batch_size = 2 ** 7
"TotalStep: 2e3, TargetReward: , UsedTime: 10s"
args.env = PreprocessEnv(env=gym.make('LunarLander-v2'))
args.net_dim = 2**8
args.batch_size = 2**8
"TotalStep: 6e4, TargetReward: 200, UsedTime: 600s"
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate__multiprocessing(args)
def demo1_discrete_action_space():
import elegantrl.agent as agent
"""DEMO 1: Discrete action env of gym"""
args = Arguments(agent=None, env=None, gpu_id=None) # see Arguments() to see hyper-parameters
'''choose an DRL algorithm'''
# args.agent_rl = agent.AgentDuelingDQN() # AgentDQN()
args.agent = agent.AgentD3QN() # AgentDoubleDQN()
'''choose environment'''
args.env = PreprocessEnv(env=gym.make('CartPole-v0'))
args.net_dim = 2 ** 7 # change a default hyper-parameters
# args.env = PreprocessEnv(env=gym.make('LunarLander-v2'))
# args.net_dim = 2 ** 8
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate__multiprocessing(args)
def demo2_continuous_action_space_off_policy():
args = Arguments(if_on_policy=False)
'''choose an DRL algorithm'''
from elegantrl.agent import AgentModSAC # AgentSAC, AgentTD3, AgentDDPG
args.agent = AgentModSAC() # AgentSAC(), AgentTD3(), AgentDDPG()
'''choose environment'''
env = gym.make('Pendulum-v0')
env.target_reward = -200 # set target_reward manually for env 'Pendulum-v0'
args.env = PreprocessEnv(env=env)
args.reward_scale = 2**-3 # RewardRange: -1800 < -200 < -50 < 0
"TotalStep: 4e5, TargetReward: -200, UsedTime: 400s"
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
"TotalStep: 9e4, TargetReward: 200, UsedTime: 2500s"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.net_dim = 2 ** 8
# args.break_step = int(2e5)
# args.if_allow_break = False
"TotalStep: 2e5, TargetReward: 300, UsedTime: 5000s"
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate__multiprocessing(args)
def train_and_evaluate(args):
args.init_before_training()
'''basic arguments'''
cwd = args.cwd
env = args.env
agent = args.agent
gpu_id = args.gpu_id # necessary for Evaluator?
'''training arguments'''
net_dim = args.net_dim
max_memo = args.max_memo
break_step = args.break_step
batch_size = args.batch_size
target_step = args.target_step
repeat_times = args.repeat_times
if_break_early = args.if_allow_break
if_per = args.if_per
gamma = args.gamma
reward_scale = args.reward_scale
'''evaluating arguments'''
eval_gap = args.eval_gap
eval_times1 = args.eval_times1
eval_times2 = args.eval_times2
if args.env_eval is not None:
env_eval = args.env_eval
elif args.env_eval in set(gym.envs.registry.env_specs.keys()):
env_eval = PreprocessEnv(gym.make(env.env_name))
else:
env_eval = deepcopy(env)
del args # In order to show these hyper-parameters clearly, I put them above.
'''init: environment'''
max_step = env.max_step
state_dim = env.state_dim
action_dim = env.action_dim
if_discrete = env.if_discrete
'''init: Agent, ReplayBuffer, Evaluator'''
agent.init(net_dim, state_dim, action_dim, if_per)
if_on_policy = getattr(agent, 'if_on_policy', False)
buffer = ReplayBuffer(max_len=max_memo + max_step,
state_dim=state_dim,
action_dim=1 if if_discrete else action_dim,
if_on_policy=if_on_policy,
if_per=if_per,
if_gpu=True)
evaluator = Evaluator(
cwd=cwd,
agent_id=gpu_id,
device=agent.device,
env=env_eval,
eval_gap=eval_gap,
eval_times1=eval_times1,
eval_times2=eval_times2,
)
'''prepare for training'''
agent.state = env.reset()
if if_on_policy:
steps = 0
else: # explore_before_training for off-policy
with torch.no_grad(): # update replay buffer
steps = explore_before_training(env, buffer, target_step,
reward_scale, gamma)
agent.update_net(buffer, target_step, batch_size,
repeat_times) # pre-training and hard update
agent.act_target.load_state_dict(agent.act.state_dict()) if getattr(
agent, 'act_target', None) else None
agent.cri_target.load_state_dict(agent.cri.state_dict()) if getattr(
agent, 'cri_target', None) else None
total_step = steps
'''start training'''
if_reach_goal = False
while not ((if_break_early and if_reach_goal) or total_step > break_step
or os.path.exists(f'{cwd}/stop')):
steps = agent.explore_env(env, buffer, target_step, reward_scale,
gamma)
total_step += steps
obj_a, obj_c = agent.update_net(buffer, target_step, batch_size,
repeat_times)
if_reach_goal = evaluator.evaluate_save(agent.act, steps, obj_a, obj_c)
evaluator.draw_plot()
print(
f'| SavedDir: {cwd}\n| UsedTime: {time.time() - evaluator.start_time:.0f}'
)
