repo_description: Fine-tuned  MARL algorithms on SMAC (100% win rates on most scenarios)
repo_topics: marl reinforcement-learning smac sota starcraft
code: from modules.agents import REGISTRY as agent_REGISTRY
  from components.action_selectors import REGISTRY as action_REGISTRY
  import torch as th
  from .basic_controller import BasicMAC
  
  
  # This multi-agent controller shares parameters between agents
  class LICAMAC(BasicMAC):
      def select_actions(self, ep_batch, t_ep, t_env, bs=slice(None), test_mode=False):
          # Only select actions for the selected batch elements in bs
          avail_actions = ep_batch["avail_actions"][:, t_ep]
          agent_outputs = self.forward(ep_batch, t_ep, test_mode=test_mode, gumbel=(not test_mode))
          return self.action_selector.select_action(agent_outputs[bs], avail_actions[bs], t_env, test_mode=test_mode)
  
      def forward(self, ep_batch, t, test_mode=False, gumbel=False):
          agent_inputs = self._build_inputs(ep_batch, t)
          avail_actions = ep_batch["avail_actions"][:, t]
          agent_outs, self.hidden_states = self.agent(agent_inputs, self.hidden_states)
  
          # Softmax the agent outputs if they're policy logits
          if self.agent_output_type == "pi_logits":
  
              if getattr(self.args, "mask_before_softmax", True):
                  # Make the logits for unavailable actions very negative to minimise their affect on the softmax
                  agent_outs = agent_outs.reshape(ep_batch.batch_size * self.n_agents, -1)
                  reshaped_avail_actions = avail_actions.reshape(ep_batch.batch_size * self.n_agents, -1)
                  agent_outs[reshaped_avail_actions == 0] = -1e5
  
              if gumbel:
                  return agent_outs.view(ep_batch.batch_size, self.n_agents, -1)
  
              agent_outs = th.nn.functional.softmax(agent_outs, dim=-1)
  
          return agent_outs.view(ep_batch.batch_size, self.n_agents, -1)
  