分布式强化学习(Distributed Reinforcement Learning):分布式算法,如IMPALA(Importance Weighted Actor-Learner Architecture)和R2D2(Recurrent Replay Distributed DQN),是近年来的重要发展。这些算法允许大规模分布式训练和数据并行化,从而提高了学习效率和可扩展性。 模型预测控制(Model Predictive Control,MPC)和基于模型强化学...
ans = rlReplayMemory with properties: MaxLength: 20000 Length: 0 Replace Agent Experience Buffer Create an environment for training the agent. For this example, load a predefined environment. env = rlPredefinedEnv("SimplePendulumWithImage-Discrete"); ...
优先级刷新(Priority Refresh):为了解决不稳定值的问题,作者提出了优先级刷新技术,即主动刷新 replay buffer 中所有数据点的优先级。如图1b所示,一组优先级刷新器定期更新 replay buffer 中所有数据点的优先级。最新的优先级以恒定频率同步到所有重分析节点。由于目标是稳定value学习的过程,作者选用TD-error作为优先级。
File "/Users/user/Desktop/ruc/paper/quant/Quant/venv/lib/python3.8/site-packages/qlib/rl/trainer/vessel.py", line 171, in train collector = Collector(self.policy, vector_env, VectorReplayBuffer(self.buffer_size, len(vector_env))) File "/Users/user/Desktop/ruc/paper/quant/Quant/venv/lib/...
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble, Lee et al, 2021.arxiv.Algorithm: Balance Replay, Pessimistic Q-Ensemble. You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL, Wonjoon Goo and Scott Niekum, 2021.CoRL.Algorithm: YOEO. ...
(num_optim_steps): - obs, next_obs, action, hidden_state, reward, done = replay_buffer.sample(batch_size) - loss = loss_fn(obs, next_obs, action, hidden_state, reward, done) + tensordict = replay_buffer.sample(batch_size) + loss = loss_fn(tensordict) loss.backward() optim.step...
Prioritized replay:基于优先级的replay机制,replay加速训练过程,变相增加样本,并且能独立于当前训练过程中状态的影响。这个replay权重还是和DQN error(下图)有关,Silver16年论文PRIORITIZED EXPERIENCE REPLAY。 Dueling network:在网络内部把Q(s,a) 分解成 V(s) + A(s, a),V(s)与动作无关,A(s, a)与动作相关...
Prioritized replay:基于优先级的replay机制,replay加速训练过程,变相增加样本,并且能独立于当前训练过程中状态的影响。这个replay权重还是和DQN error(下图)有关,Silver16年论文PRIORITIZED EXPERIENCE REPLAY。 Dueling network:在网络内部把Q(s,a) 分解成 V(s) + A(s, a),V(s)与动作无关,A(s, a)与动作相关...
请选择预览文件 连续动作空间上求解RL——DDPG 一、连续动作 二、DDPG简介 三、算法流程 四、代码实践 导入依赖 设置超参数 搭建Model、Algorithm、Agent架构 经验池 ReplayMemory Training && Test(训练&&测试) 创建环境和Agent,创建经验池,启动训练,保存模型 五、总结 ...
[5]Prioritized Experience Replay, Schaul et al, 2015.Algorithm: Prioritized Experience Replay (PER). [6]Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al, 2017.Algorithm: Rainbow DQN. b. Policy Gradients¶ [7]Asynchronous Methods for Deep Reinforcement Learning, Mnih et...