add JiDi evaluation support

add JiDi evaluation support

.gitignore

@@ -159,3 +159,4 @@ opponent_pool
 !/examples/selfplay/opponent_templates/tictactoe_opponent/info.json
 wandb_run
 examples/dmc/new.gif
+/examples/snake/submissions/rl/actor_2000.pth

Makefile

@@ -12,7 +12,7 @@ test:
 lint:
 	$(call check_install, ruff)
 	ruff ${PYTHON_FILES} --select=E9,F63,F7,F82 --show-source
-	ruff ${PYTHON_FILES} --exit-zero | grep -v '501\|405\|401\|402\|403'
+	ruff ${PYTHON_FILES} --exit-zero | grep -v '501\|405\|401\|402\|403\|722'
 
 format:
 	$(call check_install, isort)

examples/snake/README.md

@@ -7,6 +7,11 @@ This is the example for the snake game.
 python train_selfplay.py
 ```
 
+## Evaluate JiDi submissions locally
+
+```bash
+python jidi_eval.py
+```
 
 ## Submit to JiDi
 

examples/snake/jidi_eval.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+
+from openrl.arena import make_arena
+from openrl.arena.agents.jidi_agent import JiDiAgent
+from openrl.envs.wrappers.pettingzoo_wrappers import RecordWinner
+
+
+def run_arena(
+    render: bool = False,
+    parallel: bool = True,
+    seed=0,
+    total_games: int = 10,
+    max_game_onetime: int = 5,
+):
+    env_wrappers = [RecordWinner]
+
+    player_num = 3
+    arena = make_arena(
+        f"snakes_{player_num}v{player_num}", env_wrappers=env_wrappers, render=render
+    )
+
+    agent1 = JiDiAgent("./submissions/rule_v1", player_num=player_num)
+    agent2 = JiDiAgent("./submissions/rl", player_num=player_num)
+
+    arena.reset(
+        agents={"agent1": agent1, "agent2": agent2},
+        total_games=total_games,
+        max_game_onetime=max_game_onetime,
+        seed=seed,
+    )
+    result = arena.run(parallel=parallel)
+    arena.close()
+    print(result)
+    return result
+
+
+if __name__ == "__main__":
+    run_arena(render=False, parallel=True, seed=0, total_games=100, max_game_onetime=5)

examples/snake/submissions/random_agent/submission.py

@@ -26,4 +26,5 @@ def my_controller(observation, action_space, is_act_continuous):
     for i in range(len(action_space)):
         player = sample_single_dim(action_space[i], is_act_continuous)
         joint_action.append(player)
+
     return joint_action

examples/snake/submissions/rl/README.md

@@ -0,0 +1,3 @@
+# Download actor weight
+
+Please download [actor_2000.pth](https://github.com/CarlossShi/Competition_3v3snakes/tree/master/agent/rl) before use this code.

examples/snake/submissions/rl/submission.py

@@ -0,0 +1,196 @@
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from torch import nn
+from torch.distributions import Categorical
+
+HIDDEN_SIZE = 256
+device = torch.device("cpu")
+
+from typing import Union
+
+Activation = Union[str, nn.Module]
+
+_str_to_activation = {
+    "relu": torch.nn.ReLU(),
+    "tanh": nn.Tanh(),
+    "identity": nn.Identity(),
+    "softmax": nn.Softmax(dim=-1),
+}
+
+
+def mlp(
+    sizes, activation: Activation = "relu", output_activation: Activation = "identity"
+):
+    if isinstance(activation, str):
+        activation = _str_to_activation[activation]
+    if isinstance(output_activation, str):
+        output_activation = _str_to_activation[output_activation]
+
+    layers = []
+    for i in range(len(sizes) - 1):
+        act = activation if i < len(sizes) - 2 else output_activation
+        layers += [nn.Linear(sizes[i], sizes[i + 1]), act]
+    return nn.Sequential(*layers)
+
+
+def get_surrounding(state, width, height, x, y):
+    surrounding = [
+        state[(y - 1) % height][x],  # up
+        state[(y + 1) % height][x],  # down
+        state[y][(x - 1) % width],  # left
+        state[y][(x + 1) % width],
+    ]  # right
+
+    return surrounding
+
+
+def make_grid_map(
+    board_width, board_height, beans_positions: list, snakes_positions: dict
+):
+    snakes_map = [[[0] for _ in range(board_width)] for _ in range(board_height)]
+    for index, pos in snakes_positions.items():
+        for p in pos:
+            snakes_map[p[0]][p[1]][0] = index
+
+    for bean in beans_positions:
+        snakes_map[bean[0]][bean[1]][0] = 1
+
+    return snakes_map
+
+
+# Self position:        0:head_x; 1:head_y
+# Head surroundings:    2:head_up; 3:head_down; 4:head_left; 5:head_right
+# Beans positions:      (6, 7) (8, 9) (10, 11) (12, 13) (14, 15)
+# Other snake positions: (16, 17) (18, 19) (20, 21) (22, 23) (24, 25) -- (other_x - self_x, other_y - self_y)
+def get_observations(state, agents_index, obs_dim, height, width):
+    state_copy = state.copy()
+    board_width = state_copy["board_width"]
+    board_height = state_copy["board_height"]
+    beans_positions = state_copy[1]
+    snakes_positions = {
+        key: state_copy[key] for key in state_copy.keys() & {2, 3, 4, 5, 6, 7}
+    }
+    snakes_positions_list = []
+    for key, value in snakes_positions.items():
+        snakes_positions_list.append(value)
+    snake_map = make_grid_map(
+        board_width, board_height, beans_positions, snakes_positions
+    )
+    state_ = np.array(snake_map)
+    state_ = np.squeeze(state_, axis=2)
+
+    observations = np.zeros((3, obs_dim))
+    snakes_position = np.array(snakes_positions_list, dtype=object)
+    beans_position = np.array(beans_positions, dtype=object).flatten()
+    for i, element in enumerate(agents_index):
+        # # self head position
+        observations[i][:2] = snakes_positions_list[element][0][:]
+
+        # head surroundings
+        head_x = snakes_positions_list[element][0][1]
+        head_y = snakes_positions_list[element][0][0]
+
+        head_surrounding = get_surrounding(state_, width, height, head_x, head_y)
+        observations[i][2:6] = head_surrounding[:]
+
+        # beans positions
+        observations[i][6:16] = beans_position[:]
+
+        # other snake positions
+        snake_heads = np.array([snake[0] for snake in snakes_position])
+        snake_heads = np.delete(snake_heads, i, 0)
+        observations[i][16:] = snake_heads.flatten()[:]
+    return observations
+
+
+class Actor(nn.Module):
+    def __init__(self, obs_dim, act_dim, num_agents, args, output_activation="softmax"):
+        super().__init__()
+
+        self.obs_dim = obs_dim
+        self.act_dim = act_dim
+        self.num_agents = num_agents
+
+        self.args = args
+
+        sizes_prev = [obs_dim, HIDDEN_SIZE]
+        sizes_post = [HIDDEN_SIZE, HIDDEN_SIZE, act_dim]
+
+        self.prev_dense = mlp(sizes_prev)
+        self.post_dense = mlp(sizes_post, output_activation=output_activation)
+
+    def forward(self, obs_batch):
+        out = self.prev_dense(obs_batch)
+        out = self.post_dense(out)
+        return out
+
+
+class RLAgent(object):
+    def __init__(self, obs_dim, act_dim, num_agent):
+        self.obs_dim = obs_dim
+        self.act_dim = act_dim
+        self.num_agent = num_agent
+        self.device = device
+        self.output_activation = "softmax"
+        self.actor = Actor(obs_dim, act_dim, num_agent, self.output_activation).to(
+            self.device
+        )
+
+    def choose_action(self, obs):
+        obs = torch.Tensor([obs]).to(self.device)
+        logits = self.actor(obs).cpu().detach().numpy()[0]
+        return logits
+
+    def select_action_to_env(self, obs, ctrl_index):
+        logits = self.choose_action(obs)
+        actions = logits2action(logits)
+        action_to_env = to_joint_action(actions, ctrl_index)
+        return action_to_env
+
+    def load_model(self, filename):
+        self.actor.load_state_dict(torch.load(filename))
+
+
+def to_joint_action(action, ctrl_index):
+    joint_action_ = []
+    action_a = action[ctrl_index]
+    each = [0] * 4
+    each[action_a] = 1
+    joint_action_.append(each)
+    return joint_action_
+
+
+def logits2action(logits):
+    logits = torch.Tensor(logits).to(device)
+    actions = np.array([Categorical(out).sample().item() for out in logits])
+    return np.array(actions)
+
+
+agent = RLAgent(26, 4, 3)
+actor_net = os.path.dirname(os.path.abspath(__file__)) + "/actor_2000.pth"
+assert Path(actor_net).exists(), (
+    "actor_2000.pth not exists, please download from:"
+    " https://github.com/CarlossShi/Competition_3v3snakes/tree/master/agent/rl"
+)
+agent.load_model(actor_net)
+
+
+def my_controller(observation_list, action_space_list, is_act_continuous):
+    obs_dim = 26
+    obs = observation_list.copy()
+    board_width = obs["board_width"]
+    board_height = obs["board_height"]
+    o_index = obs[
+        "controlled_snake_index"
+    ]  # 2, 3, 4, 5, 6, 7 -> indexs = [0,1,2,3,4,5]
+    o_indexs_min = 3 if o_index > 4 else 0
+    indexs = [o_indexs_min, o_indexs_min + 1, o_indexs_min + 2]
+    observation = get_observations(
+        obs, indexs, obs_dim, height=board_height, width=board_width
+    )
+    actions = agent.select_action_to_env(observation, indexs.index(o_index - 2))
+    return actions