// Module: stdlib/apps/ternary_rl_agent.tern
// Purpose: Ternary Reinforcement Learning Agent
// Author: RFI-IRFOS
// Ref: https://ternlang.com
// RL Agent where exploration is an active 'tend' state.
struct RLAgent {
policy: trittensor<4 x 4>,
value: trittensor<4 x 4>
}
fn choose_action(agent: RLAgent, state: trittensor<4 x 1>) -> trit {
@sparseskip
let action_logits: trittensor<4 x 1> = agent.policy * state;
let action: trit = action_logits[0, 0];
if action == tend {
return tend; // Explore state
}
return action; // Exploit state
}