Module a2c 

Synchronous Advantage Actor-Critic (A2C) trainer.

A2C is the synchronous variant of A3C (Mnih et al. 2016, “Asynchronous Methods for Deep Reinforcement Learning”, arXiv:1602.01783): a shared actor-critic network collects short n_steps-long rollouts across num_envs parallel actors and performs a single gradient update per rollout. It lands as a sibling module to crate::train::ppo, reusing the same policy/optimizer/GAE infra with a simpler single-update-per-rollout loop.

This module is built incrementally by the A2C decomposition (#150):

• A2cConfig — hyperparameters (builder + validate()), mirroring crate::train::ppo::PPOConfig (#151).
• loss — the un-clipped policy-gradient + plain-MSE value loss (compute_a2c_policy_loss, compute_a2c_value_loss) and the A2cTrainer single-update-per-rollout loop (#152).

Re-exports

pub use config::A2cConfig;

pub use loss::compute_a2c_policy_loss;

pub use loss::compute_a2c_value_loss;

pub use trainer::A2cStats;

pub use trainer::A2cTrainer;

Modules

config

A2C configuration and hyperparameters.

loss

A2C loss math (Burn backend).

trainer

Synchronous Advantage Actor-Critic (A2C) trainer (Burn backend).