def mp_explore(args, pipe2_exp, worker_id):
args.init_before_training(if_main=False)
'''basic arguments'''
env = args.env
agent = args.agent
rollout_num = args.rollout_num
'''training arguments'''
net_dim = args.net_dim
max_memo = args.max_memo
target_step = args.target_step
gamma = args.gamma
reward_scale = args.reward_scale
random_seed = args.random_seed
torch.manual_seed(random_seed + worker_id)
np.random.seed(random_seed + worker_id)
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'''
agent.init(net_dim, state_dim, action_dim)
agent.state = env.reset()
if_on_policy = getattr(agent, 'if_on_policy', False)
buffer = ReplayBuffer(max_len=max_memo // rollout_num + max_step,
if_on_policy=if_on_policy,
state_dim=state_dim,
action_dim=1 if if_discrete else action_dim,
if_gpu=False)
'''start exploring'''
exp_step = target_step // rollout_num
with torch.no_grad():
if not if_on_policy:
explore_before_training(env, buffer, exp_step, reward_scale, gamma)
buffer.update_now_len_before_sample()
pipe2_exp.send((buffer.buf_state[:buffer.now_len],
buffer.buf_other[:buffer.now_len]))
# buf_state, buf_other = pipe1_exp.recv()
buffer.empty_buffer_before_explore()
while True:
agent.explore_env(env, buffer, exp_step, reward_scale, gamma)
buffer.update_now_len_before_sample()
pipe2_exp.send((buffer.buf_state[:buffer.now_len],
buffer.buf_other[:buffer.now_len]))
# buf_state, buf_other = pipe1_exp.recv()
buffer.empty_buffer_before_explore()
# pipe1_exp.send(agent.act)
agent.act = pipe2_exp.recv()
def __init__(self, num_agents, state_size, action_size):
self.agents = []
for i in range(num_agents):
self.agents.append(Agent(state_size, action_size))
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE)
def __init__(self, num_agents, state_size, action_size, random_seed):
self.num_agents = num_agents
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.agents = [
Agent(state_size, action_size, self.memory, BATCH_SIZE,
random_seed) for agent_posit in range(num_agents)
]
def __init__(self, action_size, state_size, shared_replay_buffer,
num_agents):
self.shared_replay_buffer = shared_replay_buffer
memory_fn = lambda: ReplayBuffer(action_size, int(1e6), BATCH_SIZE,
SEED, DEVICE)
memory = None
if shared_replay_buffer:
self.memory = memory_fn()
memory = self.memory
self.ddpg_agents = [
DDPGAgent(action_size, state_size, shared_replay_buffer, memory)
for _ in range(num_agents)
]
self.t_step = 0
def mp_explore_in_env(args, pipe2_exp, worker_id):
env = args.env
reward_scale = args.reward_scale
gamma = args.gamma
random_seed = args.random_seed
agent_rl = args.agent_rl
net_dim = args.net_dim
max_memo = args.max_memo
target_step = args.target_step
rollout_num = args.rollout_num
del args
torch.manual_seed(random_seed + worker_id)
np.random.seed(random_seed + worker_id)
'''init: env'''
state_dim = env.state_dim
action_dim = env.action_dim
if_discrete = env.if_discrete
max_step = env.max_step
'''build agent'''
agent = agent_rl(state_dim, action_dim, net_dim) # training agent
agent.state = env.reset()
# agent.device = torch.device('cpu') # env_cpu--act_cpu a little faster than env_cpu--act_gpu, but high cpu-util
'''build replay buffer, init: total_step, reward_avg'''
if_on_policy = bool(
agent_rl.__name__ in {'AgentPPO', 'AgentGaePPO', 'AgentInterPPO'})
buffer = ReplayBuffer(max_memo // rollout_num + max_step,
state_dim,
if_on_policy=if_on_policy,
action_dim=1 if if_discrete else
action_dim) # build experience replay buffer
exp_step = target_step // rollout_num
with torch.no_grad():
while True:
# pipe1_exp.send(agent.act)
agent.act = pipe2_exp.recv()
agent.update_buffer(env, buffer, exp_step, reward_scale, gamma)
buffer.update__now_len__before_sample()
pipe2_exp.send((buffer.buf_state[:buffer.now_len],
buffer.buf_other[:buffer.now_len]))
def __init__(self,
num_agents,
state_size,
action_size,
random_seed=0,
discount_factor=0.95,
tau=0.02):
super(MADDPG, self).__init__()
# self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
self.memories = [
ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
for _ in range(num_agents)
]
self.agents = [
Agent(state_size, action_size, random_seed)
for _ in range(num_agents)
]
self.discount_factor = discount_factor
self.tau = tau
self.iter = 0
def __init__(self, num_agents=2, state_size=24, action_size=2):
"""Initialize a maddpg_agent wrapper.
Params
======
num_agents (int): the number of agents in the environment
state_size (int): dimension of each state
action_size (int): dimension of each action
"""
self.num_agents = num_agents
self.state_size = state_size
self.action_size = action_size
self.agents = [
ddpg(state_size, action_size, i + 1, random_seed=0)
for i in range(num_agents)
]
# Replay memory
self.memory = ReplayBuffer(action_size,
BUFFER_SIZE,
BATCH_SIZE,
seed=0)
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?
env_eval = args.env_eval
'''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
gamma = args.gamma
reward_scale = args.reward_scale
'''evaluating arguments'''
show_gap = args.show_gap
eval_times1 = args.eval_times1
eval_times2 = args.eval_times2
env_eval = deepcopy(env) if env_eval is None else deepcopy(env_eval)
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
env_eval = deepcopy(env) if env_eval is None else deepcopy(env_eval)
'''init: Agent, ReplayBuffer, Evaluator'''
agent.init(net_dim, state_dim, action_dim)
if_on_policy = getattr(agent, 'if_on_policy', False)
buffer = ReplayBuffer(max_len=max_memo + max_step,
if_on_policy=if_on_policy,
if_gpu=True,
state_dim=state_dim,
action_dim=1 if if_discrete else action_dim)
evaluator = Evaluator(cwd=cwd,
agent_id=gpu_id,
device=agent.device,
env=env_eval,
eval_times1=eval_times1,
eval_times2=eval_times2,
show_gap=show_gap) # build Evaluator
'''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')):
with torch.no_grad(): # speed up running
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)
with torch.no_grad(): # speed up running
if_reach_goal = evaluator.evaluate_save(agent.act, steps, obj_a,
obj_c)
def train_and_evaluate(args):
args.init_before_training()
cwd = args.cwd
env = args.env
env_eval = args.env_eval
agent_id = args.gpu_id
agent_rl = args.agent_rl # basic arguments
gamma = args.gamma # training arguments
net_dim = args.net_dim
max_memo = args.max_memo
target_step = args.target_step
batch_size = args.batch_size
repeat_times = args.repeat_times
reward_scale = args.reward_scale
if_per = args.if_per
show_gap = args.show_gap # evaluate arguments
eval_times1 = args.eval_times1
eval_times2 = args.eval_times2
break_step = args.break_step
if_break_early = args.if_break_early
env_eval = deepcopy(env) if env_eval is None else deepcopy(env_eval)
del args # In order to show these hyper-parameters clearly, I put them above.
'''init: env'''
state_dim = env.state_dim
action_dim = env.action_dim
if_discrete = env.if_discrete
max_step = env.max_step
env_eval = deepcopy(env) if env_eval is None else deepcopy(env_eval)
'''init: Agent, Evaluator, ReplayBuffer'''
agent = agent_rl(net_dim, state_dim, action_dim) # build AgentRL
agent.state = env.reset()
evaluator = Evaluator(cwd=cwd,
agent_id=agent_id,
device=agent.device,
env=env_eval,
eval_times1=eval_times1,
eval_times2=eval_times2,
show_gap=show_gap) # build Evaluator
if_on_policy = agent_rl.__name__ in {'AgentPPO', 'AgentGaePPO'}
buffer = ReplayBuffer(max_memo + max_step,
state_dim,
if_on_policy=if_on_policy,
if_per=if_per,
action_dim=1 if if_discrete else
action_dim) # build experience replay buffer
if if_on_policy:
steps = 0
else:
with torch.no_grad(): # update replay buffer
steps = _explore_before_train(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 'act_target' in dir(agent) else None
total_step = steps
if_solve = False
while not ((if_break_early and if_solve) or total_step > break_step
or os.path.exists(f'{cwd}/stop')):
with torch.no_grad(): # speed up running
steps = agent.update_buffer(env, buffer, target_step, reward_scale,
gamma)
total_step += steps
obj_a, obj_c = agent.update_net(buffer, target_step, batch_size,
repeat_times)
with torch.no_grad(): # speed up running
if_solve = evaluator.evaluate_act__save_checkpoint(
agent.act, steps, obj_a, obj_c)
args = parse_arguments()
# Setup RNG
if args.seed:
print('torch RNG using seed:', args.seed)
seed = torch.manual_seed(args.seed)
# Setup Environment
env = UnityEnvironment(file_name="Banana.app", no_graphics=args.no_render)
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
# Setup Agent and Experience Replay Buffer
state_size = brain.vector_observation_space_size
action_size = brain.vector_action_space_size
memory = ReplayBuffer(action_size, args.memory, args.batch_size)
agent = BananAgent(state_size,
action_size,
memory=memory,
checkpoint_filename=args.checkpoint if args.checkpoint
and os.path.exists(args.checkpoint) else None)
print('Number of actions:', action_size)
print('State Features: ', state_size)
# Retrieve hyperparameters
epsilon = args.eps
epsilon_decay = args.eps_decay
epsilon_mininum = args.eps_min
evaluate = args.evaluate
if evaluate:
print('Running in evaluation mode!')
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.env_eval
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
train_record = agent.update_net(buffer, target_step, batch_size,
repeat_times)
if_reach_goal = evaluator.evaluate_save(agent.act, steps, train_record)
evaluator.draw_plot()
print(
f'| SavedDir: {cwd}\n| UsedTime: {time.time() - evaluator.start_time:.0f}'
)