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+BitFlip
+~~~~~~~~~~~~~~~~~~
+
+Overview
+=========
+BitFlip is a very simple little game. Assuming there are n coins, each coin has two states, the positive side is denoted as 0 and the negative side is denoted as 1. The action space is a vector of length n, and executing the nth action type represents flipping the nth coin.
+For each episode, we randomly initialize the coin state and target state. If the coin state and the target state are not the same, the reward is -1, otherwise it is 1.
+
+.. image:: ./images/bitflip.gif
+   :align: center
+
+Installation
+=============
+
+Installation Method
+--------------------
+
+The BitFlip environment does not need to be installed, it is built into DI-engine.
+
+Runnable Code Example in DI-zoo
+================================
+
+Below is a complete RL training pipeline for Bitflip environment, which uses the DQN algorithm as the policy. Please run the "bitflip_dqn_main.py" file in the "\DI-engine\dizoo\classic_control\bitflip\entry" directory as follows.
+
+.. code:: python
+
+    import os
+    import gym
+    from tensorboardX import SummaryWriter
+    from easydict import EasyDict
+    from functools import partial
+
+    from ding.config import compile_config
+    from ding.worker import BaseLearner, EpisodeSerialCollector, InteractionSerialEvaluator, EpisodeReplayBuffer
+    from ding.envs import BaseEnvManager, DingEnvWrapper
+    from ding.policy import DQNPolicy
+    from ding.model import DQN
+    from ding.utils import set_pkg_seed
+    from ding.rl_utils import get_epsilon_greedy_fn
+    from ding.reward_model import HerRewardModel
+    from dizoo.classic_control.bitflip.envs import BitFlipEnv
+    from dizoo.classic_control.bitflip.config import bitflip_pure_dqn_config, bitflip_her_dqn_config
+
+
+    def main(cfg, seed=0, max_iterations=int(1e8)):
+        cfg = compile_config(
+            cfg,
+            BaseEnvManager,
+            DQNPolicy,
+            BaseLearner,
+            EpisodeSerialCollector,
+            InteractionSerialEvaluator,
+            EpisodeReplayBuffer,
+            save_cfg=True
+        )
+        collector_env_num, evaluator_env_num = cfg.env.collector_env_num, cfg.env.evaluator_env_num
+        collector_env = BaseEnvManager(
+            env_fn=[partial(BitFlipEnv, cfg=cfg.env) for _ in range(collector_env_num)], cfg=cfg.env.manager
+        )
+        evaluator_env = BaseEnvManager(
+            env_fn=[partial(BitFlipEnv, cfg=cfg.env) for _ in range(evaluator_env_num)], cfg=cfg.env.manager
+        )
+
+        # Set random seed for all package and instance
+        collector_env.seed(seed)
+        evaluator_env.seed(seed, dynamic_seed=False)
+        set_pkg_seed(seed, use_cuda=cfg.policy.cuda)
+
+        # Set up RL Policy
+        model = DQN(**cfg.policy.model)
+        policy = DQNPolicy(cfg.policy, model=model)
+
+        # Set up collection, training and evaluation utilities
+        tb_logger = SummaryWriter(os.path.join('./{}/log/'.format(cfg.exp_name), 'serial'))
+        learner = BaseLearner(cfg.policy.learn.learner, policy.learn_mode, tb_logger, exp_name=cfg.exp_name)
+        collector = EpisodeSerialCollector(
+            cfg.policy.collect.collector, collector_env, policy.collect_mode, tb_logger, exp_name=cfg.exp_name
+        )
+        evaluator = InteractionSerialEvaluator(
+            cfg.policy.eval.evaluator, evaluator_env, policy.eval_mode, tb_logger, exp_name=cfg.exp_name
+        )
+        replay_buffer = EpisodeReplayBuffer(
+            cfg.policy.other.replay_buffer, exp_name=cfg.exp_name, instance_name='episode_buffer'
+        )
+
+        # Set up other modules, etc. epsilon greedy, hindsight experience replay
+        eps_cfg = cfg.policy.other.eps
+        epsilon_greedy = get_epsilon_greedy_fn(eps_cfg.start, eps_cfg.end, eps_cfg.decay, eps_cfg.type)
+        her_cfg = cfg.policy.other.get('her', None)
+        if her_cfg is not None:
+            her_model = HerRewardModel(her_cfg, cfg.policy.cuda)
+
+        # Training & Evaluation loop
+        for _ in range(max_iterations):
+            # Evaluating at the beginning and with specific frequency
+            if evaluator.should_eval(learner.train_iter):
+                stop, reward = evaluator.eval(learner.save_checkpoint, learner.train_iter, collector.envstep)
+                if stop:
+                    break
+            # Update other modules
+            eps = epsilon_greedy(collector.envstep)
+            # Sampling data from environments
+            new_episode = collector.collect(train_iter=learner.train_iter, policy_kwargs={'eps': eps})
+            replay_buffer.push(new_episode, cur_collector_envstep=collector.envstep)
+            # Training
+            for i in range(cfg.policy.learn.update_per_collect):
+                if her_cfg and her_model.episode_size is not None:
+                    sample_size = her_model.episode_size
+                else:
+                    sample_size = learner.policy.get_attribute('batch_size')
+                train_episode = replay_buffer.sample(sample_size, learner.train_iter)
+                if train_episode is None:
+                    break
+                train_data = []
+                if her_cfg is not None:
+                    her_episodes = []
+                    for e in train_episode:
+                        her_episodes.extend(her_model.estimate(e))
+                for e in her_episodes:
+                    train_data.extend(policy.collect_mode.get_train_sample(e))
+                learner.train(train_data, collector.envstep)
+
+
+    if __name__ == "__main__":
+        # main(bitflip_pure_dqn_config)
+        main(bitflip_her_dqn_config)
+
+
+Reference
+=====================
+- BitFlip `source code <https://github.com/NervanaSystems/gym-bit-flip/blob/master/gym_bit_flip/bit_flip.py>`__
+