Updated examples to have new features
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2 changed files with 45 additions and 17 deletions
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@ -18,21 +18,24 @@ class Value(nn.Module):
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self.action_size = action_size
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self.fc1 = rn.NoisyLinear(state_size, 64)
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self.fc_norm = nn.LayerNorm(64)
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self.value_fc = rn.NoisyLinear(64, 64)
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self.value_fc_norm = nn.LayerNorm(64)
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self.value = rn.NoisyLinear(64, 1)
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self.advantage_fc = rn.NoisyLinear(64, 64)
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self.advantage_fc_norm = nn.LayerNorm(64)
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self.advantage = rn.NoisyLinear(64, action_size)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc_norm(self.fc1(x)))
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state_value = F.relu(self.value_fc(x))
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state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
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state_value = self.value(state_value)
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advantage = F.relu(self.advantage_fc(x))
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advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
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advantage = self.advantage(advantage)
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x = state_value + advantage - advantage.mean()
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@ -49,12 +52,20 @@ config['total_evaluation_episodes'] = 10
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config['batch_size'] = 32
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config['learning_rate'] = 1e-3
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config['target_sync_tau'] = 1e-1
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config['weight_decay'] = 1e-5
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config['discount_rate'] = 0.99
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config['replay_skip'] = 0
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# How many episodes between printing out the episode stats
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config['print_stat_n_eps'] = 1
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config['disable_cuda'] = False
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# Prioritized vs Random Sampling
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# 0 - Random sampling
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# 1 - Only the highest prioirities
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config['prioritized_replay_sampling_priority'] = 0.6
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# How important are the weights for the loss?
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# 0 - Treat all losses equally
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# 1 - Lower the importance of high losses
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# Should ideally start from 0 and move your way to 1 to prevent overfitting
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
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def train(runner, agent, config, logwriter = None):
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finished = False
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@ -96,7 +107,8 @@ target_net = rn.TargetNetwork(net, device = device)
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# Actor takes a net and uses it to produce actions from given states
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actor = ArgMaxSelector(net, action_size, device = device)
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# Memory stores experiences for later training
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memory = M.ReplayMemory(capacity = config['memory_size'])
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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# memory = M.ReplayMemory(capacity = config['memory_size'])
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# Runner performs a certain number of steps in the environment
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runner = rltorch.mp.EnvironmentRun(env, actor, config, memory = memory, logger = logger, name = "Training")
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@ -17,31 +17,37 @@ class Value(nn.Module):
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self.action_size = action_size
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self.conv1 = nn.Conv2d(4, 32, kernel_size = (8, 8), stride = (4, 4))
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self.conv_norm1 = nn.LayerNorm([32, 19, 19])
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self.conv2 = nn.Conv2d(32, 64, kernel_size = (4, 4), stride = (2, 2))
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self.conv_norm2 = nn.LayerNorm([64, 8, 8])
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self.conv3 = nn.Conv2d(64, 64, kernel_size = (3, 3), stride = (1, 1))
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self.conv_norm3 = nn.LayerNorm([64, 6, 6])
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self.fc1 = rn.NoisyLinear(64 * 6 * 6, 384)
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self.fc_norm = nn.LayerNorm(384)
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self.value_fc = rn.NoisyLinear(384, 384)
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self.value_fc_norm = nn.LayerNorm(384)
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self.value = rn.NoisyLinear(384, 1)
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self.advantage_fc = rn.NoisyLinear(384, 384)
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self.advantage_fc_norm = nn.LayerNorm(384)
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self.advantage = rn.NoisyLinear(384, action_size)
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def forward(self, x):
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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x = F.relu(self.conv3(x))
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x = F.relu(self.conv_norm1(self.conv1(x)))
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x = F.relu(self.conv_norm2(self.conv2(x)))
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x = F.relu(self.conv_norm3(self.conv3(x)))
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# Makes batch_size dimension again
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x = x.view(-1, 64 * 6 * 6)
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc_norm(self.fc1(x)))
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state_value = F.relu(self.value_fc(x))
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state_value = F.relu(self.value_fc_norm(self.value_fc(x)))
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state_value = self.value(state_value)
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advantage = F.relu(self.advantage_fc(x))
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advantage = F.relu(self.advantage_fc_norm(self.advantage_fc(x)))
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advantage = self.advantage(advantage)
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x = state_value + advantage - advantage.mean()
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@ -52,24 +58,34 @@ class Value(nn.Module):
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return x
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config = {}
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config['seed'] = 901
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config['environment_name'] = 'PongNoFrameskip-v4'
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config['memory_size'] = 4000
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config['total_training_episodes'] = 50
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config['memory_size'] = 5000
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config['total_training_episodes'] = 500
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config['total_evaluation_episodes'] = 10
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config['learning_rate'] = 1e-4
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config['target_sync_tau'] = 1e-3
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config['weight_decay'] = 1e-8
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config['discount_rate'] = 0.999
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config['discount_rate'] = 0.99
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config['exploration_rate'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.1, end_value = 0.01, iterations = 5000)
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config['replay_skip'] = 4
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config['batch_size'] = 32 * (config['replay_skip'] + 1)
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# How many episodes between printing out the episode stats
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config['print_stat_n_eps'] = 1
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config['disable_cuda'] = False
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# Prioritized vs Random Sampling
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# 0 - Random sampling
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# 1 - Only the highest prioirities
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config['prioritized_replay_sampling_priority'] = 0.6
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# How important are the weights for the loss?
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# 0 - Treat all losses equally
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# 1 - Lower the importance of high losses
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# Should ideally start from 0 and move your way to 1 to prevent overfitting
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config['prioritized_replay_weight_importance'] = rltorch.scheduler.ExponentialScheduler(initial_value = 0.4, end_value = 1, iterations = 5000)
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def train(runner, agent, config, logwriter = None):
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finished = False
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@ -113,7 +129,7 @@ target_net = rn.TargetNetwork(net, device = device)
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# Actor takes a network and uses it to produce actions from given states
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actor = ArgMaxSelector(net, action_size, device = device)
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# Memory stores experiences for later training
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memory = M.ReplayMemory(capacity = config['memory_size'])
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memory = M.PrioritizedReplayMemory(capacity = config['memory_size'], alpha = config['prioritized_replay_sampling_priority'])
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# Runner performs a certain number of steps in the environment
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runner = rltorch.mp.EnvironmentRun(env, actor, config, memory = memory, logger = logger, name = "Training")
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@ -137,4 +153,4 @@ print("Evaluating...")
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rltorch.env.simulateEnvEps(env, actor, config, total_episodes = config['total_evaluation_episodes'], logger = logger, name = "Evaluation")
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print("Evaulations Done.")
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logwriter.close() # We don't need to write anything out to disk anymore
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logwriter.close() # We don't need to write anything out to disk anymore
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