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* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
49 lines
2.0 KiB
ReStructuredText
49 lines
2.0 KiB
ReStructuredText
Soft Actor-Critic
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============
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**Actions space:** Continuous
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**References:** `Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor <https://arxiv.org/abs/1801.01290>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/sac.png
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:align: center
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Algorithm Description
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---------------------
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Choosing an action - Continuous actions
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+++++++++++++++++++++++++++++++++++++
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The policy network is used in order to predict mean and log std for each action. While training, a sample is taken
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from a Gaussian distribution with these mean and std values. When testing, the agent can choose deterministically
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by picking the mean value or sample from a gaussian distribution like in training.
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Training the network
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++++++++++++++++++++
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Start by sampling a batch :math:`B` of transitions from the experience replay.
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* To train the **Q network**, use the following targets:
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.. math:: y_t^Q=r(s_t,a_t)+\gamma \cdot V(s_{t+1})
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The state value used in the above target is acquired by running the target state value network.
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* To train the **State Value network**, use the following targets:
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.. math:: y_t^V = \min_{i=1,2}Q_i(s_t,\tilde{a}) - log\pi (\tilde{a} \vert s),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t)
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The state value network is trained using a sample-based approximation of the connection between and state value and state
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action values, The actions used for constructing the target are **not** sampled from the replay buffer, but rather sampled
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from the current policy.
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* To train the **actor network**, use the following equation:
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.. math:: \nabla_{\theta} J \approx \nabla_{\theta} \frac{1}{\vert B \vert} \sum_{s_t\in B} \left( Q \left(s_t, \tilde{a}_\theta(s_t)\right) - log\pi_{\theta}(\tilde{a}_{\theta}(s_t)\vert s_t) \right),\,\,\,\, \tilde{a} \sim \pi(\cdot \vert s_t)
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After every training step, do a soft update of the V target network's weights from the online networks.
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.. autoclass:: rl_coach.agents.soft_actor_critic_agent.SoftActorCriticAlgorithmParameters |