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Add random categorical sampling of actions

def get_uniform_valid_logits(states, rng_key):
  raw_action_logits = jnp.zeros((len(states), gojax.get_action_size(states)))
  flattened_invalids = jnp.reshape(gojax.get_invalids(states),
                                    (-1, states.shape[2] * states.shape[3]))
  action_logits = jnp.where(
      jnp.append(flattened_invalids, jnp.zeros((len(states), 1), dtype=bool), axis=1),
      jnp.full_like(raw_action_logits, float('-inf')), raw_action_logits)
  action_1d = jax.random.categorical(rng_key, action_logits)
  one_hot_action_1d = jax.nn.one_hot(action_1d, action_logits.shape[1], dtype=bool)
  actions = jnp.reshape(one_hot_action_1d[:, :-1],
                        (-1, states.shape[2], states.shape[3]))
  return actions

get_winner function

takes in N x H x C x B x B history array and returns an integer array of shape (N, 1) with values in {-1, 0, 1}

  • 1 = black wins
  • 0 = tie
  • -1 = white wins

optimize next_states

Redundantly changing turns, passing setting killed pieces and ending the game.

action1d_to_indicator

Takes in action1ds and action size.

Should be JIT compatible.

Replace old function.

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