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96 lines
1.8 KiB
ReStructuredText
96 lines
1.8 KiB
ReStructuredText
.. _cem_rl:
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.. automodule:: torchy_baselines.cem_rl
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CEM RL
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======
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Combining cross-entropy method (CEM) and Twin Delayed Deep Deterministic policy gradient (TD3).
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.. rubric:: Available Policies
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.. autosummary::
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:nosignatures:
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MlpPolicy
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Notes
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-----
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- Original paper: https://arxiv.org/abs/1810.01222 and https://openreview.net/forum?id=BkeU5j0ctQ
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- Original Implementation: https://github.com/apourchot/CEM-RL
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.. note::
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CEM RL is currently implemented for TD3
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.. note::
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The default policies for CEM RL differ a bit from others MlpPolicy: it uses ReLU instead of tanh activation,
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to match the original paper
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Can I use?
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----------
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- Recurrent policies: ❌
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- Multi processing: ❌
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- Gym spaces:
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============= ====== ===========
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Space Action Observation
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============= ====== ===========
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Discrete ❌ ❌
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Box ✔️ ✔️
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MultiDiscrete ❌ ❌
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MultiBinary ❌ ❌
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============= ====== ===========
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Example
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-------
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.. code-block:: python
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import numpy as np
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from torchy_baselines import CEMRL
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from torchy_baselines.td3.policies import MlpPolicy
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# n_grad = 0 corresponds to CEM (in fact CMA-ES without history)
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model = CEMRL(MlpPolicy, 'Pendulum-v0', pop_size=10, n_grad=5, verbose=1)
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model.learn(total_timesteps=50000, log_interval=10)
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model.save("td3_pendulum")
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env = model.get_env()
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del model # remove to demonstrate saving and loading
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model = CEMRL.load("td3_pendulum")
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obs = env.reset()
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while True:
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action, _states = model.predict(obs)
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obs, rewards, dones, info = env.step(action)
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env.render()
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Parameters
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----------
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.. autoclass:: CEMRL
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:members:
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:inherited-members:
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.. _cemrl_policies:
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CEM RL Policies
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---------------
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.. autoclass:: MlpPolicy
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:members:
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:inherited-members:
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