Make SubprocVecEnv works with DummyVecEnv (#908)
* Make SubprocVecEnv works with DummyVecEnv (nested environments for synchronous sampling) * SubprocVecEnv now supports running environments in series in each process * Added docstring to the test definition * Added additional test to check, whether SubprocVecEnv results with the same output when in_series parameter is enabled and not * Added more test cases for in_series parameter * Refactored worker function, added docstring for in_series parameter * Remove check for TF presence in setup.py
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@@ -4,33 +4,36 @@ import numpy as np
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from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
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from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars
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def worker(remote, parent_remote, env_fn_wrapper):
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def worker(remote, parent_remote, env_fn_wrappers):
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def step_env(env, action):
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ob, reward, done, info = env.step(action)
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if done:
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ob = env.reset()
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return ob, reward, done, info
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parent_remote.close()
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parent_remote.close()
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env = env_fn_wrapper.x()
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envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x]
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try:
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try:
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while True:
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while True:
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cmd, data = remote.recv()
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cmd, data = remote.recv()
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if cmd == 'step':
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if cmd == 'step':
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ob, reward, done, info = env.step(data)
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remote.send([step_env(env, action) for env, action in zip(envs, data)])
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if done:
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ob = env.reset()
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remote.send((ob, reward, done, info))
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elif cmd == 'reset':
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elif cmd == 'reset':
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ob = env.reset()
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remote.send([env.reset() for env in envs])
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remote.send(ob)
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elif cmd == 'render':
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elif cmd == 'render':
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remote.send(env.render(mode='rgb_array'))
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remote.send([env.render(mode='rgb_array') for env in envs])
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elif cmd == 'close':
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elif cmd == 'close':
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remote.close()
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remote.close()
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break
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break
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elif cmd == 'get_spaces_spec':
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elif cmd == 'get_spaces_spec':
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remote.send((env.observation_space, env.action_space, env.spec))
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remote.send((envs[0].observation_space, envs[0].action_space, envs[0].spec))
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else:
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else:
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raise NotImplementedError
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raise NotImplementedError
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except KeyboardInterrupt:
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except KeyboardInterrupt:
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print('SubprocVecEnv worker: got KeyboardInterrupt')
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print('SubprocVecEnv worker: got KeyboardInterrupt')
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finally:
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finally:
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env.close()
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for env in envs:
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env.close()
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class SubprocVecEnv(VecEnv):
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class SubprocVecEnv(VecEnv):
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@@ -38,17 +41,23 @@ class SubprocVecEnv(VecEnv):
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VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
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VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes.
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Recommended to use when num_envs > 1 and step() can be a bottleneck.
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Recommended to use when num_envs > 1 and step() can be a bottleneck.
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"""
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"""
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def __init__(self, env_fns, spaces=None, context='spawn'):
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def __init__(self, env_fns, spaces=None, context='spawn', in_series=1):
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"""
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"""
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Arguments:
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Arguments:
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env_fns: iterable of callables - functions that create environments to run in subprocesses. Need to be cloud-pickleable
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env_fns: iterable of callables - functions that create environments to run in subprocesses. Need to be cloud-pickleable
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in_series: number of environments to run in series in a single process
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(e.g. when len(env_fns) == 12 and in_series == 3, it will run 4 processes, each running 3 envs in series)
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"""
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"""
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self.waiting = False
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self.waiting = False
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self.closed = False
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self.closed = False
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self.in_series = in_series
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nenvs = len(env_fns)
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nenvs = len(env_fns)
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assert nenvs % in_series == 0, "Number of envs must be divisible by number of envs to run in series"
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self.nremotes = nenvs // in_series
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env_fns = np.array_split(env_fns, self.nremotes)
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ctx = mp.get_context(context)
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ctx = mp.get_context(context)
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self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(nenvs)])
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self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(self.nremotes)])
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self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
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self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn)))
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for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
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for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)]
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for p in self.ps:
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for p in self.ps:
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@@ -61,10 +70,11 @@ class SubprocVecEnv(VecEnv):
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self.remotes[0].send(('get_spaces_spec', None))
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self.remotes[0].send(('get_spaces_spec', None))
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observation_space, action_space, self.spec = self.remotes[0].recv()
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observation_space, action_space, self.spec = self.remotes[0].recv()
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self.viewer = None
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self.viewer = None
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VecEnv.__init__(self, len(env_fns), observation_space, action_space)
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VecEnv.__init__(self, nenvs, observation_space, action_space)
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def step_async(self, actions):
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def step_async(self, actions):
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self._assert_not_closed()
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self._assert_not_closed()
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actions = np.array_split(actions, self.nremotes)
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for remote, action in zip(self.remotes, actions):
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for remote, action in zip(self.remotes, actions):
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remote.send(('step', action))
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remote.send(('step', action))
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self.waiting = True
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self.waiting = True
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@@ -72,6 +82,7 @@ class SubprocVecEnv(VecEnv):
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def step_wait(self):
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def step_wait(self):
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self._assert_not_closed()
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self._assert_not_closed()
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results = [remote.recv() for remote in self.remotes]
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results = [remote.recv() for remote in self.remotes]
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results = _flatten_list(results)
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self.waiting = False
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self.waiting = False
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obs, rews, dones, infos = zip(*results)
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obs, rews, dones, infos = zip(*results)
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return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos
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return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos
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@@ -80,7 +91,9 @@ class SubprocVecEnv(VecEnv):
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self._assert_not_closed()
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self._assert_not_closed()
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for remote in self.remotes:
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for remote in self.remotes:
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remote.send(('reset', None))
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remote.send(('reset', None))
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return _flatten_obs([remote.recv() for remote in self.remotes])
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obs = [remote.recv() for remote in self.remotes]
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obs = _flatten_list(obs)
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return _flatten_obs(obs)
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def close_extras(self):
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def close_extras(self):
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self.closed = True
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self.closed = True
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@@ -97,6 +110,7 @@ class SubprocVecEnv(VecEnv):
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for pipe in self.remotes:
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for pipe in self.remotes:
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pipe.send(('render', None))
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pipe.send(('render', None))
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imgs = [pipe.recv() for pipe in self.remotes]
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imgs = [pipe.recv() for pipe in self.remotes]
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imgs = _flatten_list(imgs)
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return imgs
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return imgs
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def _assert_not_closed(self):
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def _assert_not_closed(self):
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@@ -115,3 +129,10 @@ def _flatten_obs(obs):
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return {k: np.stack([o[k] for o in obs]) for k in keys}
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return {k: np.stack([o[k] for o in obs]) for k in keys}
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else:
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else:
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return np.stack(obs)
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return np.stack(obs)
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def _flatten_list(l):
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assert isinstance(l, (list, tuple))
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assert len(l) > 0
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assert all([len(l_) > 0 for l_ in l])
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return [l__ for l_ in l for l__ in l_]
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@@ -67,6 +67,50 @@ def test_vec_env(klass, dtype): # pylint: disable=R0914
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assert_venvs_equal(env1, env2, num_steps=num_steps)
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assert_venvs_equal(env1, env2, num_steps=num_steps)
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@pytest.mark.parametrize('dtype', ('uint8', 'float32'))
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@pytest.mark.parametrize('num_envs_in_series', (3, 4, 6))
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def test_sync_sampling(dtype, num_envs_in_series):
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"""
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Test that a SubprocVecEnv running with envs in series
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outputs the same as DummyVecEnv.
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"""
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num_envs = 12
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num_steps = 100
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shape = (3, 8)
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def make_fn(seed):
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"""
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Get an environment constructor with a seed.
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"""
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return lambda: SimpleEnv(seed, shape, dtype)
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fns = [make_fn(i) for i in range(num_envs)]
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env1 = DummyVecEnv(fns)
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env2 = SubprocVecEnv(fns, in_series=num_envs_in_series)
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assert_venvs_equal(env1, env2, num_steps=num_steps)
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@pytest.mark.parametrize('dtype', ('uint8', 'float32'))
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@pytest.mark.parametrize('num_envs_in_series', (3, 4, 6))
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def test_sync_sampling_sanity(dtype, num_envs_in_series):
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"""
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Test that a SubprocVecEnv running with envs in series
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outputs the same as SubprocVecEnv without running in series.
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"""
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num_envs = 12
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num_steps = 100
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shape = (3, 8)
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def make_fn(seed):
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"""
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Get an environment constructor with a seed.
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"""
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return lambda: SimpleEnv(seed, shape, dtype)
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fns = [make_fn(i) for i in range(num_envs)]
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env1 = SubprocVecEnv(fns)
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env2 = SubprocVecEnv(fns, in_series=num_envs_in_series)
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assert_venvs_equal(env1, env2, num_steps=num_steps)
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class SimpleEnv(gym.Env):
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class SimpleEnv(gym.Env):
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"""
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"""
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An environment with a pre-determined observation space
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An environment with a pre-determined observation space
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