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Commit b7bb7a0

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norcinorci
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Fix (#85)
* fixed bug in reward logging. due to multithread env does not have POST_EPISODE_STAGE. See: ReinforcementLearningCore\src\core\run.jl, line 46. * fixed logging error. due to @info was called multiple times without log_step_increment=0
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‎src/experiments/rl_envs.jl

Lines changed: 37 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -87,7 +87,8 @@ function RLCore.Experiment(
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end,
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DoEveryNEpisode() do t, agent, env
8989
with_logger(lg) do
90-
@info "training" reward = total_reward_per_episode.rewards[end]
90+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
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0
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end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -180,7 +181,8 @@ function RLCore.Experiment(
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end,
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DoEveryNEpisode() do t, agent, env
182183
with_logger(lg) do
183-
@info "training" reward = total_reward_per_episode.rewards[end]
184+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
185+
0
184186
end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -274,7 +276,8 @@ function RLCore.Experiment(
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end,
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DoEveryNEpisode() do t, agent, env
276278
with_logger(lg) do
277-
@info "training" reward = total_reward_per_episode.rewards[end]
279+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
280+
0
278281
end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -374,7 +377,8 @@ function RLCore.Experiment(
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end,
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DoEveryNEpisode() do t, agent, env
376379
with_logger(lg) do
377-
@info "training" reward = total_reward_per_episode.rewards[end]
380+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
381+
0
378382
end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -476,7 +480,8 @@ function RLCore.Experiment(
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end,
477481
DoEveryNEpisode() do t, agent, env
478482
with_logger(lg) do
479-
@info "training" reward = total_reward_per_episode.rewards[end]
483+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
484+
0
480485
end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -563,8 +568,14 @@ function RLCore.Experiment(
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critic_loss = agent.policy.learner.critic_loss,
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entropy_loss = agent.policy.learner.entropy_loss,
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loss = agent.policy.learner.loss,
566-
reward = total_reward_per_episode.reward[end]
567571
)
572+
for i in 1:length(env)
573+
if get_terminal(env[i])
574+
@info "training" reward = total_reward_per_episode.rewards[i][end] log_step_increment =
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0
576+
break
577+
end
578+
end
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end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -656,8 +667,14 @@ function RLCore.Experiment(
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critic_loss = agent.policy.learner.critic_loss,
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entropy_loss = agent.policy.learner.entropy_loss,
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loss = agent.policy.learner.loss,
659-
reward = total_reward_per_episode.reward[end],
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)
671+
for i in 1:length(env)
672+
if get_terminal(env[i])
673+
@info "training" reward = total_reward_per_episode.rewards[i][end] log_step_increment =
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0
675+
break
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end
677+
end
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end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -764,7 +781,8 @@ function RLCore.Experiment(
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end,
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DoEveryNEpisode() do t, agent, env
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with_logger(lg) do
767-
@info "training" reward = total_reward_per_episode.rewards[end]
784+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
785+
0
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end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -863,8 +881,14 @@ function RLCore.Experiment(
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actor_loss = agent.policy.learner.actor_loss[end, end],
864882
critic_loss = agent.policy.learner.critic_loss[end, end],
865883
loss = agent.policy.learner.loss[end, end],
866-
reward = total_reward_per_episode.reward[end]
867884
)
885+
for i in 1:length(env)
886+
if get_terminal(env[i])
887+
@info "training" reward = total_reward_per_episode.rewards[i][end] log_step_increment =
888+
0
889+
break
890+
end
891+
end
868892
end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -944,7 +968,8 @@ function RLCore.Experiment(
944968
end,
945969
DoEveryNEpisode() do t, agent, env
946970
with_logger(lg) do
947-
@info "training" reward = total_reward_per_episode.rewards[end]
971+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
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0
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end
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end,
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DoEveryNStep(10000) do t, agent, env
@@ -1038,7 +1063,8 @@ function RLCore.Experiment(
10381063
end,
10391064
DoEveryNEpisode() do t, agent, env
10401065
with_logger(lg) do
1041-
@info "training" reward = total_reward_per_episode.rewards[end]
1066+
@info "training" reward = total_reward_per_episode.rewards[end] log_step_increment =
1067+
0
10421068
end
10431069
end,
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DoEveryNStep(10000) do t, agent, env

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