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l2_regularization ( parameter, float >= 0.0) – L2 regularization loss weight.Required if baseline optimizer is specified, main objective will be used for baseline ifīaseline objective and optimizer are not specified Main optimizer will be used for baseline if none, a float implies none and specifies aīaseline optimization objective configuration, see the – Horizon of discounted-sum return estimationīaseline optimizer configuration, see the reward_estimation ( specification) – Reward estimation configuration with the following.objective ( specification) – Optimization objective configuration, see the.optimizer ( specification) – Optimizer configuration, see the.( "never" | parameter, int > 0 | 0.0 = batch_size) –
SCRIPT ERRORS MAX RECORDER UPDATE
![script errors max recorder script errors max recorder](https://www.pointwise.com/doc/user-manual/edit/images/preferences2.png)
Periodic updates based on batches of experience, and optionally employ a baseline/critic/target Which act according to a policy parametrized by a neural network, leverage a memory module for Highly configurable agent and basis for a broad class of deep reinforcement learning agents, Tensorforce agent (specification key: tensorforce). TensorforceAgent ( states, actions, update, optimizer, objective, reward_estimation, max_episode_timesteps=None, policy='auto', memory=None, baseline=None, baseline_optimizer=None, baseline_objective=None, l2_regularization=0.0, entropy_regularization=0.0, state_preprocessing='linear_normalization', exploration=0.0, variable_noise=0.0, parallel_interactions=1, config=None, saver=None, summarizer=None, tracking=None, recorder=None, **kwargs ) ¶ Tensorforce Agent ¶ class tensorforce.agents.