- fix css style error
- fix mujoco benchmark result
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n+e 2021-05-23 12:43:03 +08:00 committed by GitHub
parent 655d5fb14f
commit 458028a326
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16 changed files with 15 additions and 15 deletions

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@ -90,8 +90,6 @@ html_logo = "_static/images/tianshou-logo.png"
def setup(app): def setup(app):
app.add_js_file(
"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min.js")
app.add_js_file("https://cdn.jsdelivr.net/npm/vega@5.20.2") app.add_js_file("https://cdn.jsdelivr.net/npm/vega@5.20.2")
app.add_js_file("https://cdn.jsdelivr.net/npm/vega-lite@5.1.0") app.add_js_file("https://cdn.jsdelivr.net/npm/vega-lite@5.1.0")
app.add_js_file("https://cdn.jsdelivr.net/npm/vega-embed@6.17.0") app.add_js_file("https://cdn.jsdelivr.net/npm/vega-embed@6.17.0")

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@ -33,7 +33,7 @@ Welcome to Tianshou!
Here is Tianshou's other features: Here is Tianshou's other features:
* Elegant framework, using only ~2000 lines of code * Elegant framework, using only ~3000 lines of code
* State-of-the-art `MuJoCo benchmark <https://github.com/thu-ml/tianshou/tree/master/examples/mujoco>`_ * State-of-the-art `MuJoCo benchmark <https://github.com/thu-ml/tianshou/tree/master/examples/mujoco>`_
* Support parallel environment simulation (synchronous or asynchronous) for all algorithms: :ref:`parallel_sampling` * Support parallel environment simulation (synchronous or asynchronous) for all algorithms: :ref:`parallel_sampling`
* Support recurrent state representation in actor network and critic network (RNN-style training for POMDP): :ref:`rnn_training` * Support recurrent state representation in actor network and critic network (RNN-style training for POMDP): :ref:`rnn_training`

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@ -3,4 +3,5 @@ tqdm
torch torch
numba numba
tensorboard tensorboard
sphinx<4
sphinxcontrib-bibtex sphinxcontrib-bibtex

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@ -1,6 +1,7 @@
Benchmark Benchmark
========= =========
Mujoco Benchmark Mujoco Benchmark
---------------- ----------------

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@ -62,7 +62,7 @@ def make_atari_env_watch(args):
def test_c51(args=get_args()): def test_c51(args=get_args()):
env = make_atari_env(args) env = make_atari_env(args)
args.state_shape = env.observation_space.shape or env.observation_space.n args.state_shape = env.observation_space.shape or env.observation_space.n
args.action_shape = env.env.action_space.shape or env.env.action_space.n args.action_shape = env.action_space.shape or env.action_space.n
# should be N_FRAMES x H x W # should be N_FRAMES x H x W
print("Observations shape:", args.state_shape) print("Observations shape:", args.state_shape)
print("Actions shape:", args.action_shape) print("Actions shape:", args.action_shape)

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@ -59,7 +59,7 @@ def make_atari_env_watch(args):
def test_dqn(args=get_args()): def test_dqn(args=get_args()):
env = make_atari_env(args) env = make_atari_env(args)
args.state_shape = env.observation_space.shape or env.observation_space.n args.state_shape = env.observation_space.shape or env.observation_space.n
args.action_shape = env.env.action_space.shape or env.env.action_space.n args.action_shape = env.action_space.shape or env.action_space.n
# should be N_FRAMES x H x W # should be N_FRAMES x H x W
print("Observations shape:", args.state_shape) print("Observations shape:", args.state_shape)
print("Actions shape:", args.action_shape) print("Actions shape:", args.action_shape)

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@ -60,7 +60,7 @@ def make_atari_env_watch(args):
def test_qrdqn(args=get_args()): def test_qrdqn(args=get_args()):
env = make_atari_env(args) env = make_atari_env(args)
args.state_shape = env.observation_space.shape or env.observation_space.n args.state_shape = env.observation_space.shape or env.observation_space.n
args.action_shape = env.env.action_space.shape or env.env.action_space.n args.action_shape = env.action_space.shape or env.action_space.n
# should be N_FRAMES x H x W # should be N_FRAMES x H x W
print("Observations shape:", args.state_shape) print("Observations shape:", args.state_shape)
print("Actions shape:", args.action_shape) print("Actions shape:", args.action_shape)

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