def build_and_train(game="pong",
run_ID=0,
cuda_idx=None,
mid_batch_reset=False,
n_parallel=2):
affinity = dict(cuda_idx=cuda_idx, workers_cpus=list(range(n_parallel)))
Collector = GpuResetCollector if mid_batch_reset else GpuWaitResetCollector
print(f"To satisfy mid_batch_reset=={mid_batch_reset}, using {Collector}.")
sampler = GpuParallelSampler(
EnvCls=AtariEnv,
env_kwargs=dict(game=game,
num_img_obs=1), # Learn on individual frames.
CollectorCls=Collector,
batch_T=20, # Longer sampling/optimization horizon for recurrence.
batch_B=16, # 16 parallel environments.
max_decorrelation_steps=400,
)
algo = A2C() # Run with defaults.
agent = AtariLstmAgent()
runner = MinibatchRl(
algo=algo,
agent=agent,
sampler=sampler,
n_steps=50e6,
log_interval_steps=1e5,
affinity=affinity,
)
config = dict(game=game)
name = "a2c_" + game
log_dir = "example_4"
with logger_context(log_dir, run_ID, name, config):
runner.train()
def build_and_train(slot_affinity_code, log_dir, run_ID, config_key):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
variant = load_variant(log_dir)
config = update_config(config, variant)
sampler = GpuSampler(
EnvCls=AtariEnv,
env_kwargs=config["env"],
CollectorCls=WaitResetCollector,
TrajInfoCls=AtariTrajInfo,
**config["sampler"]
)
algo = PPO(optim_kwargs=config["optim"], **config["algo"])
agent = AtariLstmAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(
algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"]
)
name = config["env"]["game"]
with logger_context(log_dir, run_ID, name, config):
runner.train()
def build_and_train(slot_affinity_code, log_dir, run_ID, config_key):
affinity = affinity_from_code(slot_affinity_code)
config = configs[config_key]
# variant = load_variant(log_dir)
# config = update_config(config, variant)
sampler = CpuSampler(EnvCls=AtariEnv,
env_kwargs=config["env"],
CollectorCls=EpisodicLivesWaitResetCollector,
**config["sampler"])
algo = A2C(optim_kwargs=config["optim"], **config["algo"])
agent = AtariLstmAgent(model_kwargs=config["model"], **config["agent"])
runner = MinibatchRl(algo=algo,
agent=agent,
sampler=sampler,
affinity=affinity,
**config["runner"])
name = config["env"]["game"] + str(config["algo"]["entropy_loss_coeff"])
with logger_context(log_dir, run_ID, name, config):
runner.train()
def build_and_train(game="academy_empty_goal_close", run_ID=1, cuda_idx=None):
env_vector_size = args.envVectorSize
coach = Coach(envOptions=args.envOptions,
vectorSize=env_vector_size,
algo='Bandit',
initialQ=args.initialQ,
beta=args.beta)
sampler = SerialSampler(
EnvCls=create_single_football_env,
env_kwargs=dict(game=game),
eval_env_kwargs=dict(game=game),
batch_T=5, # Four time-steps per sampler iteration.
batch_B=env_vector_size,
max_decorrelation_steps=0,
eval_n_envs=args.evalNumOfEnvs,
eval_max_steps=int(10e3),
eval_max_trajectories=5,
coach=coach,
eval_env=args.evalEnv,
)
algo = PPO(minibatches=1) # Run with defaults.
agent = AtariLstmAgent() # TODO: move to ff
runner = MinibatchRlEval(
algo=algo,
agent=agent,
sampler=sampler,
n_steps=args.numOfSteps,
log_interval_steps=1e3,
affinity=dict(cuda_idx=cuda_idx),
)
name = args.name
log_dir = "example_1"
with logger_context(log_dir,
run_ID,
name,
log_params=vars(args),
snapshot_mode="last"):
runner.train()
def start_experiment(args):
args_json = json.dumps(vars(args), indent=4)
if not os.path.isdir(args.log_dir):
os.makedirs(args.log_dir)
with open(args.log_dir + '/arguments.json', 'w') as jsonfile:
jsonfile.write(args_json)
with open(args.log_dir + '/git.txt', 'w') as git_file:
branch = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD']).strip().decode('utf-8')
commit = subprocess.check_output(['git', 'rev-parse', 'HEAD']).strip().decode('utf-8')
git_file.write('{}/{}'.format(branch, commit))
config = dict(env_id=args.env)
if args.sample_mode == 'gpu':
# affinity = dict(num_gpus=args.num_gpus, workers_cpus=list(range(args.num_cpus)))
if args.num_gpus > 0:
# import ipdb; ipdb.set_trace()
affinity = make_affinity(
run_slot=0,
n_cpu_core=args.num_cpus, # Use 16 cores across all experiments.
n_gpu=args.num_gpus, # Use 8 gpus across all experiments.
# contexts_per_gpu=2,
# hyperthread_offset=72, # If machine has 24 cores.
# n_socket=2, # Presume CPU socket affinity to lower/upper half GPUs.
gpu_per_run=args.gpu_per_run, # How many GPUs to parallelize one run across.
# cpu_per_run=1,
)
print('Make multi-gpu affinity')
else:
affinity = dict(cuda_idx=0, workers_cpus=list(range(args.num_cpus)))
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
else:
affinity = dict(workers_cpus=list(range(args.num_cpus)))
# potentially reload models
initial_optim_state_dict = None
initial_model_state_dict = None
if args.pretrain != 'None':
os.system(f"find {args.log_dir} -name '*.json' -delete") # clean up json files for video recorder
checkpoint = torch.load(os.path.join(_RESULTS_DIR, args.pretrain, 'params.pkl'))
initial_optim_state_dict = checkpoint['optimizer_state_dict']
initial_model_state_dict = checkpoint['agent_state_dict']
# ----------------------------------------------------- POLICY ----------------------------------------------------- #
model_args = dict(curiosity_kwargs=dict(curiosity_alg=args.curiosity_alg), curiosity_step_kwargs=dict())
if args.curiosity_alg =='icm':
model_args['curiosity_kwargs']['feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs']['prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs']['forward_loss_wt'] = args.forward_loss_wt
model_args['curiosity_kwargs']['forward_model'] = args.forward_model
model_args['curiosity_kwargs']['feature_space'] = args.feature_space
elif args.curiosity_alg == 'micm':
model_args['curiosity_kwargs']['feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs']['prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs']['forward_loss_wt'] = args.forward_loss_wt
model_args['curiosity_kwargs']['forward_model'] = args.forward_model
model_args['curiosity_kwargs']['ensemble_mode'] = args.ensemble_mode
model_args['curiosity_kwargs']['device'] = args.sample_mode
elif args.curiosity_alg == 'disagreement':
model_args['curiosity_kwargs']['feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['ensemble_size'] = args.ensemble_size
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs']['prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs']['forward_loss_wt'] = args.forward_loss_wt
model_args['curiosity_kwargs']['device'] = args.sample_mode
model_args['curiosity_kwargs']['forward_model'] = args.forward_model
elif args.curiosity_alg == 'ndigo':
model_args['curiosity_kwargs']['feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['pred_horizon'] = args.pred_horizon
model_args['curiosity_kwargs']['prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs']['device'] = args.sample_mode
elif args.curiosity_alg == 'rnd':
model_args['curiosity_kwargs']['feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs']['drop_probability'] = args.drop_probability
model_args['curiosity_kwargs']['gamma'] = args.discount
model_args['curiosity_kwargs']['device'] = args.sample_mode
if args.curiosity_alg != 'none':
model_args['curiosity_step_kwargs']['curiosity_step_minibatches'] = args.curiosity_step_minibatches
if args.env in _MUJOCO_ENVS:
if args.lstm:
agent = MujocoLstmAgent(initial_model_state_dict=initial_model_state_dict)
else:
agent = MujocoFfAgent(initial_model_state_dict=initial_model_state_dict)
else:
if args.lstm:
agent = AtariLstmAgent(
initial_model_state_dict=initial_model_state_dict,
model_kwargs=model_args,
no_extrinsic=args.no_extrinsic,
dual_model=args.dual_model,
)
else:
agent = AtariFfAgent(initial_model_state_dict=initial_model_state_dict,
model_kwargs=model_args,
no_extrinsic=args.no_extrinsic,
dual_model=args.dual_model)
# ----------------------------------------------------- LEARNING ALG ----------------------------------------------------- #
if args.alg == 'ppo':
algo = PPO(
discount=args.discount,
learning_rate=args.lr,
value_loss_coeff=args.v_loss_coeff,
entropy_loss_coeff=args.entropy_loss_coeff,
OptimCls=torch.optim.Adam,
optim_kwargs=None,
clip_grad_norm=args.grad_norm_bound,
initial_optim_state_dict=initial_optim_state_dict, # is None is not reloading a checkpoint
gae_lambda=args.gae_lambda,
minibatches=args.minibatches, # if recurrent: batch_B needs to be at least equal, if not recurrent: batch_B*batch_T needs to be at least equal to this
epochs=args.epochs,
ratio_clip=args.ratio_clip,
linear_lr_schedule=args.linear_lr,
normalize_advantage=args.normalize_advantage,
normalize_reward=args.normalize_reward,
curiosity_type=args.curiosity_alg,
policy_loss_type=args.policy_loss_type
)
elif args.alg == 'a2c':
algo = A2C(
discount=args.discount,
learning_rate=args.lr,
value_loss_coeff=args.v_loss_coeff,
entropy_loss_coeff=args.entropy_loss_coeff,
OptimCls=torch.optim.Adam,
optim_kwargs=None,
clip_grad_norm=args.grad_norm_bound,
initial_optim_state_dict=initial_optim_state_dict,
gae_lambda=args.gae_lambda,
normalize_advantage=args.normalize_advantage
)
# ----------------------------------------------------- SAMPLER ----------------------------------------------------- #
# environment setup
traj_info_cl = TrajInfo # environment specific - potentially overriden below
if 'mario' in args.env.lower():
env_cl = mario_make
env_args = dict(
game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000
)
elif args.env in _PYCOLAB_ENVS:
env_cl = deepmind_make
traj_info_cl = PycolabTrajInfo
env_args = dict(
game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000,
log_heatmaps=args.log_heatmaps,
logdir=args.log_dir,
obs_type=args.obs_type,
grayscale=args.grayscale,
max_steps_per_episode=args.max_episode_steps
)
elif args.env in _MUJOCO_ENVS:
env_cl = gym_make
env_args = dict(
id=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=False,
normalize_obs_steps=10000
)
elif args.env in _ATARI_ENVS:
env_cl = AtariEnv
traj_info_cl = AtariTrajInfo
env_args = dict(
game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000,
downsampling_scheme='classical',
record_freq=args.record_freq,
record_dir=args.log_dir,
horizon=args.max_episode_steps,
score_multiplier=args.score_multiplier,
repeat_action_probability=args.repeat_action_probability,
fire_on_reset=args.fire_on_reset
)
if args.sample_mode == 'gpu':
if args.lstm:
collector_class = GpuWaitResetCollector
else:
collector_class = GpuResetCollector
sampler = GpuSampler(
EnvCls=env_cl,
env_kwargs=env_args,
eval_env_kwargs=env_args,
batch_T=args.timestep_limit,
batch_B=args.num_envs,
max_decorrelation_steps=0,
TrajInfoCls=traj_info_cl,
eval_n_envs=args.eval_envs,
eval_max_steps=args.eval_max_steps,
eval_max_trajectories=args.eval_max_traj,
record_freq=args.record_freq,
log_dir=args.log_dir,
CollectorCls=collector_class
)
else:
if args.lstm:
collector_class = CpuWaitResetCollector
else:
collector_class = CpuResetCollector
sampler = CpuSampler(
EnvCls=env_cl,
env_kwargs=env_args,
eval_env_kwargs=env_args,
batch_T=args.timestep_limit, # timesteps in a trajectory episode
batch_B=args.num_envs, # environments distributed across workers
max_decorrelation_steps=0,
TrajInfoCls=traj_info_cl,
eval_n_envs=args.eval_envs,
eval_max_steps=args.eval_max_steps,
eval_max_trajectories=args.eval_max_traj,
record_freq=args.record_freq,
log_dir=args.log_dir,
CollectorCls=collector_class
)
# ----------------------------------------------------- RUNNER ----------------------------------------------------- #
if args.eval_envs > 0:
runner = (MinibatchRlEval if args.num_gpus <= 1 else SyncRlEval)(
algo=algo,
agent=agent,
sampler=sampler,
n_steps=args.iterations,
affinity=affinity,
log_interval_steps=args.log_interval,
log_dir=args.log_dir,
pretrain=args.pretrain
)
else:
runner = (MinibatchRl if args.num_gpus <= 1 else SyncRl)(
algo=algo,
agent=agent,
sampler=sampler,
n_steps=args.iterations,
affinity=affinity,
log_interval_steps=args.log_interval,
log_dir=args.log_dir,
pretrain=args.pretrain
)
with logger_context(args.log_dir, config, snapshot_mode="last", use_summary_writer=True):
runner.train()
def build_and_train(windx,
windy,
game="pong",
run_ID=0,
cuda_idx=None,
sample_mode="serial",
n_parallel=2,
num_envs=2,
eval=False,
train_mask=[True, True],
wandb_log=False,
save_models_to_wandb=False,
log_interval_steps=1e5,
alt_train=False,
n_steps=50e6,
max_episode_length=np.inf,
b_size=5,
max_decor_steps=10):
# def envs:
# player_model_kwargs = dict(hidden_sizes=[32], lstm_size=16, nonlinearity=torch.nn.ReLU, normalize_observation=False,
# norm_obs_clip=10, norm_obs_var_clip=1e-6)
# observer_model_kwargs = dict(hidden_sizes=[128], lstm_size=16, nonlinearity=torch.nn.ReLU,
# normalize_observation=False, norm_obs_clip=10, norm_obs_var_clip=1e-6)
player_reward_shaping = None
observer_reward_shaping = None
window_size = np.asarray([windx, windy])
affinity = dict(cuda_idx=cuda_idx, workers_cpus=list(range(n_parallel)))
gpu_cpu = "CPU" if cuda_idx is None else f"GPU {cuda_idx}"
if sample_mode == "serial":
alt = False
Sampler = SerialSampler # (Ignores workers_cpus.)
if eval:
eval_collector_cl = SerialEvalCollector
else:
eval_collector_cl = None
print(f"Using serial sampler, {gpu_cpu} for sampling and optimizing.")
elif sample_mode == "cpu":
alt = False
Sampler = CpuSampler
if eval:
eval_collector_cl = CpuEvalCollector
else:
eval_collector_cl = None
print(
f"Using CPU parallel sampler (agent in workers), {gpu_cpu} for optimizing."
)
env_kwargs = dict(env_name=game,
window_size=window_size,
player_reward_shaping=player_reward_shaping,
observer_reward_shaping=observer_reward_shaping,
max_episode_length=max_episode_length)
if eval:
eval_env_kwargs = env_kwargs
eval_max_steps = 1e4
num_eval_envs = num_envs
else:
eval_env_kwargs = None
eval_max_steps = None
num_eval_envs = 0
sampler = Sampler(
EnvCls=CWTO_EnvWrapperAtari,
env_kwargs=env_kwargs,
batch_T=b_size,
batch_B=num_envs,
max_decorrelation_steps=max_decor_steps,
eval_n_envs=num_eval_envs,
eval_CollectorCls=eval_collector_cl,
eval_env_kwargs=eval_env_kwargs,
eval_max_steps=eval_max_steps,
)
player_algo = A2C()
observer_algo = A2C()
player = AtariLstmAgent() #model_kwargs=player_model_kwargs)
observer = CWTO_AtariLstmAgent() #model_kwargs=observer_model_kwargs)
agent = CWTO_AgentWrapper(player, observer, alt=alt, train_mask=train_mask)
if eval:
RunnerCl = MinibatchRlEval
else:
RunnerCl = MinibatchRl
runner = RunnerCl(player_algo=player_algo,
observer_algo=observer_algo,
agent=agent,
sampler=sampler,
n_steps=n_steps,
log_interval_steps=log_interval_steps,
affinity=affinity,
wandb_log=wandb_log,
alt_train=alt_train)
config = dict(domain=game)
log_dir = os.getcwd() + "/cwto_logs/" + game
with logger_context(log_dir, run_ID, game, config):
runner.train()
if save_models_to_wandb:
agent.save_models_to_wandb()
def start_experiment(args):
args_json = json.dumps(vars(args), indent=4)
if not os.path.isdir(args.log_dir):
os.makedirs(args.log_dir)
with open(args.log_dir + '/arguments.json', 'w') as jsonfile:
jsonfile.write(args_json)
config = dict(env_id=args.env)
if args.sample_mode == 'gpu':
assert args.num_gpus > 0
affinity = dict(cuda_idx=0, workers_cpus=list(range(args.num_cpus)))
os.environ['CUDA_VISIBLE_DEVICES'] = str(0)
else:
affinity = dict(workers_cpus=list(range(args.num_cpus)))
# potentially reload models
initial_optim_state_dict = None
initial_model_state_dict = None
if args.pretrain != 'None':
os.system(f"find {args.log_dir} -name '*.json' -delete"
) # clean up json files for video recorder
checkpoint = torch.load(
os.path.join(_RESULTS_DIR, args.pretrain, 'params.pkl'))
initial_optim_state_dict = checkpoint['optimizer_state_dict']
initial_model_state_dict = checkpoint['agent_state_dict']
# ----------------------------------------------------- POLICY ----------------------------------------------------- #
model_args = dict(curiosity_kwargs=dict(curiosity_alg=args.curiosity_alg))
if args.curiosity_alg == 'icm':
model_args['curiosity_kwargs'][
'feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs'][
'prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs'][
'forward_loss_wt'] = args.forward_loss_wt
elif args.curiosity_alg == 'disagreement':
model_args['curiosity_kwargs'][
'feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['ensemble_size'] = args.ensemble_size
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs'][
'prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs'][
'forward_loss_wt'] = args.forward_loss_wt
model_args['curiosity_kwargs']['device'] = args.sample_mode
elif args.curiosity_alg == 'ndigo':
model_args['curiosity_kwargs'][
'feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs']['pred_horizon'] = args.pred_horizon
model_args['curiosity_kwargs']['batch_norm'] = args.batch_norm
model_args['curiosity_kwargs']['num_predictors'] = args.num_predictors
model_args['curiosity_kwargs']['device'] = args.sample_mode
elif args.curiosity_alg == 'rnd':
model_args['curiosity_kwargs'][
'feature_encoding'] = args.feature_encoding
model_args['curiosity_kwargs'][
'prediction_beta'] = args.prediction_beta
model_args['curiosity_kwargs'][
'drop_probability'] = args.drop_probability
model_args['curiosity_kwargs']['gamma'] = args.discount
model_args['curiosity_kwargs']['device'] = args.sample_mode
if args.env in _MUJOCO_ENVS:
if args.lstm:
agent = MujocoLstmAgent(
initial_model_state_dict=initial_model_state_dict)
else:
agent = MujocoFfAgent(
initial_model_state_dict=initial_model_state_dict)
else:
if args.lstm:
agent = AtariLstmAgent(
initial_model_state_dict=initial_model_state_dict,
model_kwargs=model_args,
no_extrinsic=args.no_extrinsic)
else:
agent = AtariFfAgent(
initial_model_state_dict=initial_model_state_dict)
# ----------------------------------------------------- LEARNING ALG ----------------------------------------------------- #
if args.alg == 'ppo':
if args.kernel_mu == 0.:
kernel_params = None
else:
kernel_params = (args.kernel_mu, args.kernel_sigma)
algo = PPO(
discount=args.discount,
learning_rate=args.lr,
value_loss_coeff=args.v_loss_coeff,
entropy_loss_coeff=args.entropy_loss_coeff,
OptimCls=torch.optim.Adam,
optim_kwargs=None,
clip_grad_norm=args.grad_norm_bound,
initial_optim_state_dict=
initial_optim_state_dict, # is None is not reloading a checkpoint
gae_lambda=args.gae_lambda,
minibatches=args.
minibatches, # if recurrent: batch_B needs to be at least equal, if not recurrent: batch_B*batch_T needs to be at least equal to this
epochs=args.epochs,
ratio_clip=args.ratio_clip,
linear_lr_schedule=args.linear_lr,
normalize_advantage=args.normalize_advantage,
normalize_reward=args.normalize_reward,
kernel_params=kernel_params,
curiosity_type=args.curiosity_alg)
elif args.alg == 'a2c':
algo = A2C(discount=args.discount,
learning_rate=args.lr,
value_loss_coeff=args.v_loss_coeff,
entropy_loss_coeff=args.entropy_loss_coeff,
OptimCls=torch.optim.Adam,
optim_kwargs=None,
clip_grad_norm=args.grad_norm_bound,
initial_optim_state_dict=initial_optim_state_dict,
gae_lambda=args.gae_lambda,
normalize_advantage=args.normalize_advantage)
# ----------------------------------------------------- SAMPLER ----------------------------------------------------- #
# environment setup
traj_info_cl = TrajInfo # environment specific - potentially overriden below
if 'mario' in args.env.lower():
env_cl = mario_make
env_args = dict(game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000)
elif 'deepmind' in args.env.lower(): # pycolab deepmind environments
env_cl = deepmind_make
traj_info_cl = PycolabTrajInfo
env_args = dict(game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000,
log_heatmaps=args.log_heatmaps,
logdir=args.log_dir,
obs_type=args.obs_type,
max_steps_per_episode=args.max_episode_steps)
elif args.env in _MUJOCO_ENVS:
env_cl = gym_make
env_args = dict(id=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=False,
normalize_obs_steps=10000)
elif args.env in _ATARI_ENVS:
env_cl = AtariEnv
traj_info_cl = AtariTrajInfo
env_args = dict(
game=args.env,
no_extrinsic=args.no_extrinsic,
no_negative_reward=args.no_negative_reward,
normalize_obs=args.normalize_obs,
normalize_obs_steps=10000,
downsampling_scheme='classical',
record_freq=args.record_freq,
record_dir=args.log_dir,
horizon=args.max_episode_steps,
)
if args.sample_mode == 'gpu':
if args.lstm:
collector_class = GpuWaitResetCollector
else:
collector_class = GpuResetCollector
sampler = GpuSampler(EnvCls=env_cl,
env_kwargs=env_args,
eval_env_kwargs=env_args,
batch_T=args.timestep_limit,
batch_B=args.num_envs,
max_decorrelation_steps=0,
TrajInfoCls=traj_info_cl,
eval_n_envs=args.eval_envs,
eval_max_steps=args.eval_max_steps,
eval_max_trajectories=args.eval_max_traj,
record_freq=args.record_freq,
log_dir=args.log_dir,
CollectorCls=collector_class)
else:
if args.lstm:
collector_class = CpuWaitResetCollector
else:
collector_class = CpuResetCollector
sampler = CpuSampler(
EnvCls=env_cl,
env_kwargs=env_args,
eval_env_kwargs=env_args,
batch_T=args.timestep_limit, # timesteps in a trajectory episode
batch_B=args.num_envs, # environments distributed across workers
max_decorrelation_steps=0,
TrajInfoCls=traj_info_cl,
eval_n_envs=args.eval_envs,
eval_max_steps=args.eval_max_steps,
eval_max_trajectories=args.eval_max_traj,
record_freq=args.record_freq,
log_dir=args.log_dir,
CollectorCls=collector_class)
# ----------------------------------------------------- RUNNER ----------------------------------------------------- #
if args.eval_envs > 0:
runner = MinibatchRlEval(algo=algo,
agent=agent,
sampler=sampler,
n_steps=args.iterations,
affinity=affinity,
log_interval_steps=args.log_interval,
log_dir=args.log_dir,
pretrain=args.pretrain)
else:
runner = MinibatchRl(algo=algo,
agent=agent,
sampler=sampler,
n_steps=args.iterations,
affinity=affinity,
log_interval_steps=args.log_interval,
log_dir=args.log_dir,
pretrain=args.pretrain)
with logger_context(args.log_dir,
config,
snapshot_mode="last",
use_summary_writer=True):
runner.train()