211 lines
9.6 KiB
Python
211 lines
9.6 KiB
Python
import os
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import time
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import numpy as np
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import os.path as osp
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from baselines import logger
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from collections import deque
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from baselines.common import explained_variance, set_global_seeds
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from baselines.common.policies import build_policy
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try:
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from mpi4py import MPI
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except ImportError:
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MPI = None
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from baselines.ppo2.runner import Runner
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def constfn(val):
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def f(_):
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return val
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return f
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def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
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vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
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log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
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save_interval=0, load_path=None, model_fn=None, **network_kwargs):
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'''
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Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
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Parameters:
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----------
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network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
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specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
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tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
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neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
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See common/models.py/lstm for more details on using recurrent nets in policies
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env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
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The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
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nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
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nenv is number of environment copies simulated in parallel)
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total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
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ent_coef: float policy entropy coefficient in the optimization objective
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lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
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training and 0 is the end of the training.
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vf_coef: float value function loss coefficient in the optimization objective
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max_grad_norm: float or None gradient norm clipping coefficient
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gamma: float discounting factor
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lam: float advantage estimation discounting factor (lambda in the paper)
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log_interval: int number of timesteps between logging events
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nminibatches: int number of training minibatches per update. For recurrent policies,
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should be smaller or equal than number of environments run in parallel.
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noptepochs: int number of training epochs per update
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cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
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and 0 is the end of the training
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save_interval: int number of timesteps between saving events
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load_path: str path to load the model from
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**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
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For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
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'''
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set_global_seeds(seed)
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if isinstance(lr, float): lr = constfn(lr)
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else: assert callable(lr)
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if isinstance(cliprange, float): cliprange = constfn(cliprange)
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else: assert callable(cliprange)
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total_timesteps = int(total_timesteps)
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policy = build_policy(env, network, **network_kwargs)
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# Get the nb of env
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nenvs = env.num_envs
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# Get state_space and action_space
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ob_space = env.observation_space
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ac_space = env.action_space
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# Calculate the batch_size
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nbatch = nenvs * nsteps
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nbatch_train = nbatch // nminibatches
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# Instantiate the model object (that creates act_model and train_model)
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if model_fn is None:
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from baselines.ppo2.model import Model
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model_fn = Model
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model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
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nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
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max_grad_norm=max_grad_norm)
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if load_path is not None:
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model.load(load_path)
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# Instantiate the runner object
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runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
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if eval_env is not None:
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eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
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epinfobuf = deque(maxlen=100)
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if eval_env is not None:
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eval_epinfobuf = deque(maxlen=100)
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# Start total timer
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tfirststart = time.time()
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nupdates = total_timesteps//nbatch
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for update in range(1, nupdates+1):
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assert nbatch % nminibatches == 0
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# Start timer
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tstart = time.time()
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frac = 1.0 - (update - 1.0) / nupdates
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# Calculate the learning rate
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lrnow = lr(frac)
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# Calculate the cliprange
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cliprangenow = cliprange(frac)
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# Get minibatch
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obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
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if eval_env is not None:
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eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
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epinfobuf.extend(epinfos)
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if eval_env is not None:
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eval_epinfobuf.extend(eval_epinfos)
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# Here what we're going to do is for each minibatch calculate the loss and append it.
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mblossvals = []
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if states is None: # nonrecurrent version
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# Index of each element of batch_size
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# Create the indices array
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inds = np.arange(nbatch)
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for _ in range(noptepochs):
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# Randomize the indexes
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np.random.shuffle(inds)
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# 0 to batch_size with batch_train_size step
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for start in range(0, nbatch, nbatch_train):
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end = start + nbatch_train
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mbinds = inds[start:end]
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slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
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mblossvals.append(model.train(lrnow, cliprangenow, *slices))
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else: # recurrent version
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assert nenvs % nminibatches == 0
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envsperbatch = nenvs // nminibatches
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envinds = np.arange(nenvs)
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flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
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for _ in range(noptepochs):
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np.random.shuffle(envinds)
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for start in range(0, nenvs, envsperbatch):
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end = start + envsperbatch
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mbenvinds = envinds[start:end]
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mbflatinds = flatinds[mbenvinds].ravel()
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slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
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mbstates = states[mbenvinds]
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mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
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# Feedforward --> get losses --> update
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lossvals = np.mean(mblossvals, axis=0)
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# End timer
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tnow = time.time()
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# Calculate the fps (frame per second)
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fps = int(nbatch / (tnow - tstart))
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if update % log_interval == 0 or update == 1:
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# Calculates if value function is a good predicator of the returns (ev > 1)
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# or if it's just worse than predicting nothing (ev =< 0)
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ev = explained_variance(values, returns)
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logger.logkv("serial_timesteps", update*nsteps)
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logger.logkv("nupdates", update)
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logger.logkv("total_timesteps", update*nbatch)
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logger.logkv("fps", fps)
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logger.logkv("explained_variance", float(ev))
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logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
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logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
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if eval_env is not None:
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logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
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logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
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logger.logkv('time_elapsed', tnow - tfirststart)
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for (lossval, lossname) in zip(lossvals, model.loss_names):
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logger.logkv(lossname, lossval)
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if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
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logger.dumpkvs()
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if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
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checkdir = osp.join(logger.get_dir(), 'checkpoints')
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os.makedirs(checkdir, exist_ok=True)
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savepath = osp.join(checkdir, '%.5i'%update)
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print('Saving to', savepath)
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model.save(savepath)
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return model
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# Avoid division error when calculate the mean (in our case if epinfo is empty returns np.nan, not return an error)
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def safemean(xs):
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return np.nan if len(xs) == 0 else np.mean(xs)
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