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Training#

Jumanji provides a training script train.py to train an online agent on a specified Jumanji environment given an environment-specific network.

Agents#

Jumanji provides two example agents in jumanji/training/agents/ to get you started with training on Jumanji environments:

  • Random agent: uses the action mask to randomly sample valid actions.

  • A2C agent: online advantage actor-critic agent that follows from [Mnih et al., 2016].

Configuration#

In each environment-specific config YAML file, you will see a "training" section like below:

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training:
    num_epochs: 1000
    num_learner_steps_per_epoch: 50
    n_steps: 20
    total_batch_size: 64
Here,

  • num_epochs corresponds to the number of data points in your plots. An epoch can be thought as an iteration.

  • num_learner_steps_per_epoch is the number of learner steps that happen in each epoch. After every learner step, the A2C agent's policy is updated.

  • n_steps is the sequence length (consecutive environment steps in a batch).

  • total_batch_size is the number of environments that are run in parallel.

So in the above example,

  • 64 environments are running in parallel.

  • Each of these 64 environments run 20 environment steps. After this, the agent's policy is updated via SGD. This constitutes a single learner step.

  • 50 such learner steps are done for the epoch in question. After this, evaluation is done using the updated policy.

  • The above procedure is done for 1000 epochs.

Evaluation#

Two types of evaluation are recorded:

  • Stochastic evaluation (same policy used during training)

  • Greedy evaluation (argmax over the action logits)

Last update: 2024-11-01
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