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@ -90,8 +90,6 @@ html_logo = "_static/images/tianshou-logo.png"
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def setup(app):
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def setup(app):
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app.add_js_file(
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"https://cdnjs.cloudflare.com/ajax/libs/jquery/3.6.0/jquery.min.js")
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app.add_js_file("https://cdn.jsdelivr.net/npm/vega@5.20.2")
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app.add_js_file("https://cdn.jsdelivr.net/npm/vega@5.20.2")
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app.add_js_file("https://cdn.jsdelivr.net/npm/vega-lite@5.1.0")
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app.add_js_file("https://cdn.jsdelivr.net/npm/vega-lite@5.1.0")
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app.add_js_file("https://cdn.jsdelivr.net/npm/vega-embed@6.17.0")
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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!
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Here is Tianshou's other features:
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Here is Tianshou's other features:
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* Elegant framework, using only ~2000 lines of code
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* Elegant framework, using only ~3000 lines of code
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* State-of-the-art `MuJoCo benchmark <https://github.com/thu-ml/tianshou/tree/master/examples/mujoco>`_
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* State-of-the-art `MuJoCo benchmark <https://github.com/thu-ml/tianshou/tree/master/examples/mujoco>`_
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* Support parallel environment simulation (synchronous or asynchronous) for all algorithms: :ref:`parallel_sampling`
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* Support parallel environment simulation (synchronous or asynchronous) for all algorithms: :ref:`parallel_sampling`
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* Support recurrent state representation in actor network and critic network (RNN-style training for POMDP): :ref:`rnn_training`
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* 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
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torch
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torch
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numba
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numba
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tensorboard
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tensorboard
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sphinx<4
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sphinxcontrib-bibtex
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sphinxcontrib-bibtex
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@ -1,6 +1,7 @@
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Benchmark
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Benchmark
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=========
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=========
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Mujoco Benchmark
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Mujoco Benchmark
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----------------
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----------------
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@ -62,7 +62,7 @@ def make_atari_env_watch(args):
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def test_c51(args=get_args()):
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def test_c51(args=get_args()):
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env = make_atari_env(args)
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env = make_atari_env(args)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.env.action_space.shape or env.env.action_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# should be N_FRAMES x H x W
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# should be N_FRAMES x H x W
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print("Observations shape:", args.state_shape)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Actions shape:", args.action_shape)
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@ -59,7 +59,7 @@ def make_atari_env_watch(args):
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def test_dqn(args=get_args()):
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def test_dqn(args=get_args()):
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env = make_atari_env(args)
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env = make_atari_env(args)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.env.action_space.shape or env.env.action_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# should be N_FRAMES x H x W
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# should be N_FRAMES x H x W
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print("Observations shape:", args.state_shape)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Actions shape:", args.action_shape)
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@ -60,7 +60,7 @@ def make_atari_env_watch(args):
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def test_qrdqn(args=get_args()):
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def test_qrdqn(args=get_args()):
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env = make_atari_env(args)
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env = make_atari_env(args)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.env.action_space.shape or env.env.action_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# should be N_FRAMES x H x W
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# should be N_FRAMES x H x W
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print("Observations shape:", args.state_shape)
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print("Observations shape:", args.state_shape)
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print("Actions shape:", args.action_shape)
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print("Actions shape:", args.action_shape)
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