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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
61 lines
2.5 KiB
ReStructuredText
61 lines
2.5 KiB
ReStructuredText
ACER
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============
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**Actions space:** Discrete
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**References:** `Sample Efficient Actor-Critic with Experience Replay <https://arxiv.org/abs/1611.01224>`_
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Network Structure
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-----------------
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.. image:: /_static/img/design_imgs/acer.png
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:width: 500px
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:align: center
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Algorithm Description
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---------------------
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Choosing an action - Discrete actions
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+++++++++++++++++++++++++++++++++++++
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The policy network is used in order to predict action probabilites. While training, a sample is taken from a categorical
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distribution assigned with these probabilities. When testing, the action with the highest probability is used.
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Training the network
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++++++++++++++++++++
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Each iteration perform one on-policy update with a batch of the last :math:`T_{max}` transitions,
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and :math:`n` (replay ratio) off-policy updates from batches of :math:`T_{max}` transitions sampled from the replay buffer.
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Each update perform the following procedure:
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1. **Calculate state values:**
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.. math:: V(s_t) = \mathbb{E}_{a \sim \pi} [Q(s_t,a)]
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2. **Calculate Q retrace:**
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.. math:: Q^{ret}(s_t,a_t) = r_t +\gamma \bar{\rho}_{t+1}[Q^{ret}(s_{t+1},a_{t+1}) - Q(s_{t+1},a_{t+1})] + \gamma V(s_{t+1})
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.. math:: \text{where} \quad \bar{\rho}_{t} = \min{\left\{c,\rho_t\right\}},\quad \rho_t=\frac{\pi (a_t \mid s_t)}{\mu (a_t \mid s_t)}
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3. **Accumulate gradients:**
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:math:`\bullet` **Policy gradients (with bias correction):**
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.. math:: \hat{g}_t^{policy} & = & \bar{\rho}_{t} \nabla \log \pi (a_t \mid s_t) [Q^{ret}(s_t,a_t) - V(s_t)] \\
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& & + \mathbb{E}_{a \sim \pi} \left(\left[\frac{\rho_t(a)-c}{\rho_t(a)}\right] \nabla \log \pi (a \mid s_t) [Q(s_t,a) - V(s_t)] \right)
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:math:`\bullet` **Q-Head gradients (MSE):**
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.. math:: \hat{g}_t^{Q} = (Q^{ret}(s_t,a_t) - Q(s_t,a_t)) \nabla Q(s_t,a_t)\\
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4. **(Optional) Trust region update:** change the policy loss gradient w.r.t network output:
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.. math:: \hat{g}_t^{trust-region} = \hat{g}_t^{policy} - \max \left\{0, \frac{k^T \hat{g}_t^{policy} - \delta}{\lVert k \rVert_2^2}\right\} k
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.. math:: \text{where} \quad k = \nabla D_{KL}[\pi_{avg} \parallel \pi]
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The average policy network is an exponential moving average of the parameters of the network (:math:`\theta_{avg}=\alpha\theta_{avg}+(1-\alpha)\theta`).
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The goal of the trust region update is to the difference between the updated policy and the average policy to ensure stability.
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.. autoclass:: rl_coach.agents.acer_agent.ACERAlgorithmParameters |