Value Networks and Critics#
Value networks estimate the value of states or state-action pairs.
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General class for value functions in RL. |
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Abstract base class for value normalisers. |
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PopArt-style EMA value normaliser. |
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Exact running mean / variance (Welford's online algorithm). |
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Dueling CNN Q-network. |
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Distributional Deep Q-Network softmax layer. |
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A convolutional neural network. |
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Specification for one agent group used by |
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Centralised critic that conditions on observations from multiple agent groups. |
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A multi-layer perceptron. |
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DDPG Convolutional Actor class. |
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DDPG Convolutional Q-value class. |
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DDPG Actor class. |
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DDPG Q-value MLP class. |
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An embedder for an LSTM module. |
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An embedder for an GRU module. |
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Canonicalize only the union of RNN keys used by |
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Context manager for setting RNNs recurrent mode. |
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Online Decision Transformer Actor class. |
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Decision Transformer Actor class. |
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Online Decision Transformer. |
Value transforms#
Value transforms compress large reward and return scales into a numerically convenient prediction space and provide the inverse mapping needed before bootstrapping. They can be composed to build custom invertible mappings.
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Abstract base class for invertible scalar value transforms. |
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Leave scalar values unchanged. |
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Symmetric-log value transform used by DreamerV3. |
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Signed-hyperbolic transform for large-magnitude value targets. |
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Compose value transforms while preserving the inverse mapping. |
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Apply the element-wise symmetric logarithm transform. |
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Apply the inverse symmetric exponential transform element-wise. |
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Apply the signed hyperbolic value transform. |
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Apply the inverse of |