def a_vs_b(ship_a, ship_b, agent_a, agent_b, ship_a_hull, trials, attack_range): """This function runs multiple trials of ship_a firing upon ship_b. Args: ship_a ((Ship, str)): Attacker and hull zone tuple. ship_b ((Ship, str)): Defender and hull zone tuple. agent_a (BaseAgent): Agent to control the actions of ship a. agent_b (BaseAgent): Agent to control the actions of ship b. ship_a_hull (str) : Attacking hull zone. trials (int): Number of trials in average calculation. range (str): Attack range. Returns: List[(str, world_state or attack effect tuple)] """ state_log = [] failures = 0 for _ in range(trials): # Reset ship b for each trial ship_b.reset() world_state = WorldState() world_state.addShip(ship_a, 0) world_state.addShip(ship_b, 1) # Begin at round 1 world_state.round = 1 # Don't attempt forever in the case of some catastrophic reoccurring error. attempts = 0 while ship_b.damage_cards() < ship_b.hull( ) and world_state.round <= ArmadaPhases.max_rounds: attempts += 1 # Handle the attack and receive the updated world state #try: world_state = handleAttack(world_state=world_state, attacker=(ship_a, ship_a_hull), defender=(ship_b, "front"), attack_range=attack_range, offensive_agent=agent_a, defensive_agent=agent_b, state_log=state_log) # Record the final state with the incremented round number. world_state.setPhase("status phase", "increment round number") world_state.round += 1 state_log.append(('state', world_state.clone())) #except RuntimeError as err: # # This is fine, the random agent will do illegal things plenty of times # pass if 250 == attempts: raise RuntimeError("Too many failures for ship firing simulation.") return state_log
def test_defense_tokens_model(spend_defense_tokens_model): """Test basic network learning loop. Test lifetime predictions during the spend defense tokens phase. """ network, errors, eval_errors = spend_defense_tokens_model network.eval() phase_name = "attack - spend defense tokens" world_size = Encodings.calculateWorldStateSize() action_size = Encodings.calculateActionSize(phase_name) attack_size = Encodings.calculateAttackSize() input_size = world_size + action_size + attack_size target_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") batch_size = 32 batch = torch.Tensor(batch_size, input_size).to(target_device) print("Eval errors for B were {}".format(eval_errors)) #print("First and last errors in b are {} and {}".format(eval_errors[0], eval_errors[-1])) assert eval_errors[0] > eval_errors[-1] # Let's examine predictions for different dice pools and spent defense tokens. # Go through the following scenarios: # 1.1 An attack with more than enough damage to destroy the ship # 1.2 The same attack but a brace that would prevent destruction # 1.3 The same attack but a redirect that would prevent destruction # Result: 1.1 should have lower lifetime than 1.2 and 1.3 # 2.1 An attack that can barely destroy the ship # 2.2 An attack that barely will not destroy the ship # Result: 2.1 should have lower lifetime than 2.2. # Ideally 1.1 and 2.1 would predict the current round. world_state = WorldState() world_state.round = 1 ship_a = Ship(name="Ship A", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=1) ship_b = Ship(name="Ship B", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=2) world_state.addShip(ship_a, 0) world_state.addShip(ship_b, 1) pool_colors, pool_faces = ['black'] * 4, ['hit_crit'] * 4 world_state.setPhase("ship phase", phase_name) # Set the front hull zone to 2 shields ship_b.get_range('shields')[ArmadaTypes.hull_zones.index('front')] = 2 # Set the hull to 3 (by assigning damage to reduce the remaining hull to 3) ship_b.set('damage', ship_b.get('hull') - 3) attack = AttackState('short', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) action_encoding = torch.cat((Encodings.encodeWorldState(world_state), Encodings.encodeAction(world_state.sub_phase, None))) state_encoding = Encodings.encodeAttackState(world_state) batch[0] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) action = [("brace", (ArmadaTypes.green, None))] action_encoding = torch.cat((Encodings.encodeWorldState(world_state), Encodings.encodeAction(world_state.sub_phase, action))) state_encoding = Encodings.encodeAttackState(world_state) batch[1] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) world_state = WorldState() world_state.round = 1 ship_a = Ship(name="Ship A", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=1) ship_b = Ship(name="Ship B", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=2) world_state.addShip(ship_a, 0) world_state.addShip(ship_b, 1) pool_colors, pool_faces = ['black'] * 4, ['hit_crit'] * 2 + ['hit'] * 2 world_state.setPhase("ship phase", phase_name) # Set the front hull zone to 2 shields ship_b.get_range('shields')[ArmadaTypes.hull_zones.index('front')] = 2 # Set the hull to 3 (by assigning damage to reduce the remaining hull to 3) ship_b.set('damage', ship_b.get('hull') - 3) attack = AttackState('short', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) action = [("redirect", (ArmadaTypes.green, [('left', 4)]))] action_encoding = torch.cat((Encodings.encodeWorldState(world_state), Encodings.encodeAction(world_state.sub_phase, action))) state_encoding = Encodings.encodeAttackState(world_state) batch[2] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) round_status = network(batch[:3]) print("super cool estimated rounds of destructions are {}".format(round_status[:3])) # Using no defense token results in destruction, the final round should be less assert(round_status[0].item() < round_status[1].item()) assert(round_status[0].item() < round_status[2].item())
def test_resolve_attack_effects_model(resolve_attack_effects_model): """Test basic network learning loop. Test lifetime predictions during the resolve attack effects phase. """ network, errors, eval_errors = resolve_attack_effects_model network.eval() phase_name = "attack - resolve attack effects" world_size = Encodings.calculateWorldStateSize() action_size = Encodings.calculateActionSize(phase_name) attack_size = Encodings.calculateAttackSize() input_size = world_size + action_size + attack_size # First verify that errors decreased during training. # print("Errors for A were {}".format(errors)) print("Eval errors for A were {}".format(eval_errors)) assert eval_errors[0] > eval_errors[-1] # Let's examine predictions for different ranges and hull zones. target_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") batch_size = 4 batch = torch.Tensor(batch_size, input_size).to(target_device) # Let's examine predictions for different dice pools and spent defense tokens. # Go through the following scenarios: # 1.1 An attack upon a ship with only 1 hull remaining # 1.2 The same dice pool but on a ship with full hull # 1.3 A dice pool with only blank dice # 1.4 A dice pool with only blanks when attacking at long range. # Create a state from resolve attack effects and an empty action. world_state = WorldState() world_state.round = 1 ship_a = Ship(name="Ship A", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=1) ship_b = Ship(name="Ship B", template=ship_templates["All Defense Tokens"], upgrades=[], player_number=2) world_state.addShip(ship_a, 0) world_state.addShip(ship_b, 1) pool_colors, pool_faces = ['black'] * 4, ['hit_crit'] * 4 world_state.setPhase("ship phase", "attack - resolve attack effects") ship_b.set('damage', ship_b.get('hull') - 1) attack = AttackState('short', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) action_encoding = torch.cat((Encodings.encodeWorldState(world_state), Encodings.encodeAction(world_state.sub_phase, None))) state_encoding = Encodings.encodeAttackState(world_state) batch[0] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) # Same dice pool but the defender has full hull ship_b.set('damage', 0) attack = AttackState('short', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) action_encoding = torch.cat((Encodings.encodeWorldState(world_state), Encodings.encodeAction(world_state.sub_phase, None))) state_encoding = Encodings.encodeAttackState(world_state) batch[1] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) # Full hull and all blanks pool_colors, pool_faces = ['black'] * 4, ['blank'] * 4 world_state.setPhase("ship phase", "attack - resolve attack effects") attack = AttackState('short', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) state_encoding = Encodings.encodeAttackState(world_state) batch[2] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) # Full hull, all blanks, firing at red range pool_colors, pool_faces = ['red'] * 2, ['blank'] * 2 world_state.setPhase("ship phase", "attack - resolve attack effects") attack = AttackState('long', ship_a, 'left', ship_b, 'front', pool_colors, pool_faces) world_state.updateAttack(attack) state_encoding = Encodings.encodeAttackState(world_state) batch[3] = torch.cat( (action_encoding.to(target_device), state_encoding.to(target_device))) lifetime_out = network(batch) print("super cool attack effects round estimates are {}".format(lifetime_out)) # The lifetimes should go up sequentially with the above scenarios. # However if the ship won't be destroyed the NN can't make an accurate relative number so be # lenient once lifetimes go above round 6. The first scenario should result in destruction # however. assert(lifetime_out[0].item() < 6) for i in range(batch.size(0) - 1): assert(lifetime_out[i].item() < lifetime_out[i+1].item() or (lifetime_out[i].item() > 6. and lifetime_out[i+1].item() > 6.))